diff --git a/.github/scripts/run-librispeech-streaming-zipformer-2023-05-18.sh b/.github/scripts/run-librispeech-streaming-zipformer-2023-05-18.sh index 45324cb270..f4e2124b15 100755 --- a/.github/scripts/run-librispeech-streaming-zipformer-2023-05-18.sh +++ b/.github/scripts/run-librispeech-streaming-zipformer-2023-05-18.sh @@ -23,6 +23,7 @@ ls -lh $repo/test_wavs/*.wav pushd $repo/exp git lfs pull --include "data/lang_bpe_500/bpe.model" +git lfs pull --include "data/lang_bpe_500/tokens.txt" git lfs pull --include "exp/jit_script_chunk_16_left_128.pt" git lfs pull --include "exp/pretrained.pt" ln -s pretrained.pt epoch-99.pt @@ -33,7 +34,7 @@ log "Export to torchscript model" ./zipformer/export.py \ --exp-dir $repo/exp \ --use-averaged-model false \ - --bpe-model $repo/data/lang_bpe_500/bpe.model \ + --tokens $repo/data/lang_bpe_500/tokens.txt \ --causal 1 \ --chunk-size 16 \ --left-context-frames 128 \ @@ -46,7 +47,7 @@ ls -lh $repo/exp/*.pt log "Decode with models exported by torch.jit.script()" ./zipformer/jit_pretrained_streaming.py \ - --bpe-model $repo/data/lang_bpe_500/bpe.model \ + --tokens $repo/data/lang_bpe_500/tokens.txt \ --nn-model-filename $repo/exp/jit_script_chunk_16_left_128.pt \ $repo/test_wavs/1089-134686-0001.wav @@ -60,7 +61,7 @@ for method in greedy_search modified_beam_search fast_beam_search; do --method $method \ --beam-size 4 \ --checkpoint $repo/exp/pretrained.pt \ - --bpe-model $repo/data/lang_bpe_500/bpe.model \ + --tokens $repo/data/lang_bpe_500/tokens.txt \ $repo/test_wavs/1089-134686-0001.wav \ $repo/test_wavs/1221-135766-0001.wav \ $repo/test_wavs/1221-135766-0002.wav diff --git a/.github/scripts/run-librispeech-zipformer-2023-05-18.sh b/.github/scripts/run-librispeech-zipformer-2023-05-18.sh index 6aac1793e8..fb1a0149d1 100755 --- a/.github/scripts/run-librispeech-zipformer-2023-05-18.sh +++ b/.github/scripts/run-librispeech-zipformer-2023-05-18.sh @@ -23,6 +23,7 @@ ls -lh $repo/test_wavs/*.wav pushd $repo/exp git lfs pull --include "data/lang_bpe_500/bpe.model" +git lfs pull --include "data/lang_bpe_500/tokens.txt" git lfs pull --include "exp/jit_script.pt" git lfs pull --include "exp/pretrained.pt" ln -s pretrained.pt epoch-99.pt @@ -33,7 +34,7 @@ log "Export to torchscript model" ./zipformer/export.py \ --exp-dir $repo/exp \ --use-averaged-model false \ - --bpe-model $repo/data/lang_bpe_500/bpe.model \ + --tokens $repo/data/lang_bpe_500/tokens.txt \ --epoch 99 \ --avg 1 \ --jit 1 @@ -43,7 +44,7 @@ ls -lh $repo/exp/*.pt log "Decode with models exported by torch.jit.script()" ./zipformer/jit_pretrained.py \ - --bpe-model $repo/data/lang_bpe_500/bpe.model \ + --tokens $repo/data/lang_bpe_500/tokens.txt \ --nn-model-filename $repo/exp/jit_script.pt \ $repo/test_wavs/1089-134686-0001.wav \ $repo/test_wavs/1221-135766-0001.wav \ @@ -56,7 +57,7 @@ for method in greedy_search modified_beam_search fast_beam_search; do --method $method \ --beam-size 4 \ --checkpoint $repo/exp/pretrained.pt \ - --bpe-model $repo/data/lang_bpe_500/bpe.model \ + --tokens $repo/data/lang_bpe_500/tokens.txt \ $repo/test_wavs/1089-134686-0001.wav \ $repo/test_wavs/1221-135766-0001.wav \ $repo/test_wavs/1221-135766-0002.wav diff --git a/.github/scripts/run-librispeech-zipformer-ctc-2023-06-14.sh b/.github/scripts/run-librispeech-zipformer-ctc-2023-06-14.sh index cfa9c420c0..0026d21095 100755 --- a/.github/scripts/run-librispeech-zipformer-ctc-2023-06-14.sh +++ b/.github/scripts/run-librispeech-zipformer-ctc-2023-06-14.sh @@ -23,6 +23,7 @@ ls -lh $repo/test_wavs/*.wav pushd $repo/exp git lfs pull --include "data/lang_bpe_500/bpe.model" +git lfs pull --include "data/lang_bpe_500/tokens.txt" git lfs pull --include "data/lang_bpe_500/HLG.pt" git lfs pull --include "data/lang_bpe_500/L.pt" git lfs pull --include "data/lang_bpe_500/LG.pt" @@ -40,7 +41,7 @@ log "Export to torchscript model" --use-transducer 1 \ --use-ctc 1 \ --use-averaged-model false \ - --bpe-model $repo/data/lang_bpe_500/bpe.model \ + --tokens $repo/data/lang_bpe_500/tokens.txt \ --epoch 99 \ --avg 1 \ --jit 1 @@ -51,7 +52,7 @@ log "Decode with models exported by torch.jit.script()" for method in ctc-decoding 1best; do ./zipformer/jit_pretrained_ctc.py \ - --bpe-model $repo/data/lang_bpe_500/bpe.model \ + --tokens $repo/data/lang_bpe_500/tokens.txt \ --model-filename $repo/exp/jit_script.pt \ --HLG $repo/data/lang_bpe_500/HLG.pt \ --words-file $repo/data/lang_bpe_500/words.txt \ @@ -71,8 +72,7 @@ for method in ctc-decoding 1best; do --use-ctc 1 \ --method $method \ --checkpoint $repo/exp/pretrained.pt \ - --bpe-model $repo/data/lang_bpe_500/bpe.model \ - --words-file $repo/data/lang_bpe_500/words.txt \ + --tokens $repo/data/lang_bpe_500/tokens.txt \ --HLG $repo/data/lang_bpe_500/HLG.pt \ --G $repo/data/lm/G_4_gram.pt \ --words-file $repo/data/lang_bpe_500/words.txt \ diff --git a/.github/scripts/test-ncnn-export.sh b/.github/scripts/test-ncnn-export.sh index 52491d2eab..ac16131d02 100755 --- a/.github/scripts/test-ncnn-export.sh +++ b/.github/scripts/test-ncnn-export.sh @@ -195,14 +195,14 @@ git lfs pull --include "data/lang_char_bpe/Linv.pt" git lfs pull --include "exp/pretrained.pt" cd exp -ln -s pretrained.pt epoch-99.pt +ln -s pretrained.pt epoch-9999.pt popd ./pruned_transducer_stateless7_streaming/export-for-ncnn-zh.py \ --lang-dir $repo/data/lang_char_bpe \ --exp-dir $repo/exp \ --use-averaged-model 0 \ - --epoch 99 \ + --epoch 9999 \ --avg 1 \ --decode-chunk-len 32 \ --num-encoder-layers "2,4,3,2,4" \ diff --git a/egs/aishell/ASR/pruned_transducer_stateless7_bbpe/decode.py b/egs/aishell/ASR/pruned_transducer_stateless7_bbpe/decode.py index fcb0ebc4e9..da90001647 100755 --- a/egs/aishell/ASR/pruned_transducer_stateless7_bbpe/decode.py +++ b/egs/aishell/ASR/pruned_transducer_stateless7_bbpe/decode.py @@ -397,7 +397,6 @@ def decode_one_batch( beam=params.beam, max_contexts=params.max_contexts, max_states=params.max_states, - subtract_ilme=True, ilme_scale=params.ilme_scale, ) for hyp in hyp_tokens: diff --git a/egs/librispeech/ASR/pruned_transducer_stateless2/beam_search.py b/egs/librispeech/ASR/pruned_transducer_stateless2/beam_search.py index 1bbad69465..17b63a6596 100644 --- a/egs/librispeech/ASR/pruned_transducer_stateless2/beam_search.py +++ b/egs/librispeech/ASR/pruned_transducer_stateless2/beam_search.py @@ -22,6 +22,7 @@ import k2 import sentencepiece as spm import torch +from torch import nn from icefall import ContextGraph, ContextState, NgramLm, NgramLmStateCost from icefall.decode import Nbest, one_best_decoding @@ -35,7 +36,6 @@ get_texts, get_texts_with_timestamp, ) -from torch import nn def fast_beam_search_one_best( @@ -47,8 +47,8 @@ def fast_beam_search_one_best( max_states: int, max_contexts: int, temperature: float = 1.0, - subtract_ilme: bool = False, - ilme_scale: float = 0.1, + ilme_scale: float = 0.0, + blank_penalty: float = 0.0, return_timestamps: bool = False, ) -> Union[List[List[int]], DecodingResults]: """It limits the maximum number of symbols per frame to 1. @@ -90,8 +90,8 @@ def fast_beam_search_one_best( max_states=max_states, max_contexts=max_contexts, temperature=temperature, - subtract_ilme=subtract_ilme, ilme_scale=ilme_scale, + blank_penalty=blank_penalty, ) best_path = one_best_decoding(lattice) @@ -114,6 +114,8 @@ def fast_beam_search_nbest_LG( nbest_scale: float = 0.5, use_double_scores: bool = True, temperature: float = 1.0, + blank_penalty: float = 0.0, + ilme_scale: float = 0.0, return_timestamps: bool = False, ) -> Union[List[List[int]], DecodingResults]: """It limits the maximum number of symbols per frame to 1. @@ -168,6 +170,8 @@ def fast_beam_search_nbest_LG( max_states=max_states, max_contexts=max_contexts, temperature=temperature, + blank_penalty=blank_penalty, + ilme_scale=ilme_scale, ) nbest = Nbest.from_lattice( @@ -240,6 +244,7 @@ def fast_beam_search_nbest( nbest_scale: float = 0.5, use_double_scores: bool = True, temperature: float = 1.0, + blank_penalty: float = 0.0, return_timestamps: bool = False, ) -> Union[List[List[int]], DecodingResults]: """It limits the maximum number of symbols per frame to 1. @@ -293,6 +298,7 @@ def fast_beam_search_nbest( beam=beam, max_states=max_states, max_contexts=max_contexts, + blank_penalty=blank_penalty, temperature=temperature, ) @@ -331,6 +337,7 @@ def fast_beam_search_nbest_oracle( use_double_scores: bool = True, nbest_scale: float = 0.5, temperature: float = 1.0, + blank_penalty: float = 0.0, return_timestamps: bool = False, ) -> Union[List[List[int]], DecodingResults]: """It limits the maximum number of symbols per frame to 1. @@ -389,6 +396,7 @@ def fast_beam_search_nbest_oracle( max_states=max_states, max_contexts=max_contexts, temperature=temperature, + blank_penalty=blank_penalty, ) nbest = Nbest.from_lattice( @@ -432,8 +440,8 @@ def fast_beam_search( max_states: int, max_contexts: int, temperature: float = 1.0, - subtract_ilme: bool = False, - ilme_scale: float = 0.1, + ilme_scale: float = 0.0, + blank_penalty: float = 0.0, ) -> k2.Fsa: """It limits the maximum number of symbols per frame to 1. @@ -503,8 +511,13 @@ def fast_beam_search( project_input=False, ) logits = logits.squeeze(1).squeeze(1) + + if blank_penalty != 0: + logits[:, 0] -= blank_penalty + log_probs = (logits / temperature).log_softmax(dim=-1) - if subtract_ilme: + + if ilme_scale != 0: ilme_logits = model.joiner( torch.zeros_like( current_encoder_out, device=current_encoder_out.device @@ -513,8 +526,11 @@ def fast_beam_search( project_input=False, ) ilme_logits = ilme_logits.squeeze(1).squeeze(1) + if blank_penalty != 0: + ilme_logits[:, 0] -= blank_penalty ilme_log_probs = (ilme_logits / temperature).log_softmax(dim=-1) log_probs -= ilme_scale * ilme_log_probs + decoding_streams.advance(log_probs) decoding_streams.terminate_and_flush_to_streams() lattice = decoding_streams.format_output(encoder_out_lens.tolist()) @@ -526,6 +542,7 @@ def greedy_search( model: nn.Module, encoder_out: torch.Tensor, max_sym_per_frame: int, + blank_penalty: float = 0.0, return_timestamps: bool = False, ) -> Union[List[int], DecodingResults]: """Greedy search for a single utterance. @@ -595,6 +612,9 @@ def greedy_search( ) # logits is (1, 1, 1, vocab_size) + if blank_penalty != 0: + logits[:, :, :, 0] -= blank_penalty + y = logits.argmax().item() if y not in (blank_id, unk_id): hyp.append(y) @@ -626,6 +646,7 @@ def greedy_search_batch( model: nn.Module, encoder_out: torch.Tensor, encoder_out_lens: torch.Tensor, + blank_penalty: float = 0, return_timestamps: bool = False, ) -> Union[List[List[int]], DecodingResults]: """Greedy search in batch mode. It hardcodes --max-sym-per-frame=1. @@ -703,6 +724,10 @@ def greedy_search_batch( logits = logits.squeeze(1).squeeze(1) # (batch_size, vocab_size) assert logits.ndim == 2, logits.shape + + if blank_penalty != 0: + logits[:, 0] -= blank_penalty + y = logits.argmax(dim=1).tolist() emitted = False for i, v in enumerate(y): @@ -923,6 +948,7 @@ def modified_beam_search( context_graph: Optional[ContextGraph] = None, beam: int = 4, temperature: float = 1.0, + blank_penalty: float = 0.0, return_timestamps: bool = False, ) -> Union[List[List[int]], DecodingResults]: """Beam search in batch mode with --max-sym-per-frame=1 being hardcoded. @@ -1028,6 +1054,9 @@ def modified_beam_search( logits = logits.squeeze(1).squeeze(1) # (num_hyps, vocab_size) + if blank_penalty != 0: + logits[:, 0] -= blank_penalty + log_probs = (logits / temperature).log_softmax(dim=-1) # (num_hyps, vocab_size) log_probs.add_(ys_log_probs) @@ -1662,6 +1691,7 @@ def beam_search( encoder_out: torch.Tensor, beam: int = 4, temperature: float = 1.0, + blank_penalty: float = 0.0, return_timestamps: bool = False, ) -> Union[List[int], DecodingResults]: """ @@ -1758,6 +1788,9 @@ def beam_search( project_input=False, ) + if blank_penalty != 0: + logits[:, :, :, 0] -= blank_penalty + # TODO(fangjun): Scale the blank posterior log_prob = (logits / temperature).log_softmax(dim=-1) # log_prob is (1, 1, 1, vocab_size) diff --git a/egs/librispeech/ASR/zipformer/export-onnx-streaming.py b/egs/librispeech/ASR/zipformer/export-onnx-streaming.py index 8cec098697..80dc19b37a 100755 --- a/egs/librispeech/ASR/zipformer/export-onnx-streaming.py +++ b/egs/librispeech/ASR/zipformer/export-onnx-streaming.py @@ -1,6 +1,6 @@ #!/usr/bin/env python3 # -# Copyright 2023 Xiaomi Corporation (Author: Fangjun Kuang) +# Copyright 2023 Xiaomi Corporation (Author: Fangjun Kuang, Wei Kang) # Copyright 2023 Danqing Fu (danqing.fu@gmail.com) """ @@ -19,7 +19,7 @@ repo=$(basename $repo_url) pushd $repo -git lfs pull --include "data/lang_bpe_500/bpe.model" +git lfs pull --include "data/lang_bpe_500/tokens.txt" git lfs pull --include "exp/pretrained.pt" cd exp @@ -29,7 +29,7 @@ 2. Export the model to ONNX ./zipformer/export-onnx-streaming.py \ - --bpe-model $repo/data/lang_bpe_500/bpe.model \ + --tokens $repo/data/lang_bpe_500/tokens.txt \ --use-averaged-model 0 \ --epoch 99 \ --avg 1 \ @@ -57,9 +57,9 @@ It will generate the following 3 files inside $repo/exp: - - encoder-epoch-99-avg-1.onnx - - decoder-epoch-99-avg-1.onnx - - joiner-epoch-99-avg-1.onnx + - encoder-epoch-99-avg-1-chunk-16-left-64.onnx + - decoder-epoch-99-avg-1-chunk-16-left-64.onnx + - joiner-epoch-99-avg-1-chunk-16-left-64.onnx See ./onnx_pretrained-streaming.py for how to use the exported ONNX models. """ @@ -69,14 +69,15 @@ from pathlib import Path from typing import Dict, List, Tuple +import k2 import onnx -import sentencepiece as spm import torch import torch.nn as nn from decoder import Decoder +from export import num_tokens from onnxruntime.quantization import QuantType, quantize_dynamic from scaling_converter import convert_scaled_to_non_scaled -from train import add_model_arguments, get_params, get_model +from train import add_model_arguments, get_model, get_params from zipformer import Zipformer2 from icefall.checkpoint import ( @@ -85,7 +86,7 @@ find_checkpoints, load_checkpoint, ) -from icefall.utils import str2bool, make_pad_mask +from icefall.utils import make_pad_mask, str2bool def get_parser(): @@ -142,10 +143,10 @@ def get_parser(): ) parser.add_argument( - "--bpe-model", + "--tokens", type=str, - default="data/lang_bpe_500/bpe.model", - help="Path to the BPE model", + default="data/lang_bpe_500/tokens.txt", + help="Path to the tokens.txt", ) parser.add_argument( @@ -585,12 +586,9 @@ def main(): logging.info(f"device: {device}") - sp = spm.SentencePieceProcessor() - sp.load(params.bpe_model) - - # is defined in local/train_bpe_model.py - params.blank_id = sp.piece_to_id("") - params.vocab_size = sp.get_piece_size() + token_table = k2.SymbolTable.from_file(params.tokens) + params.blank_id = token_table[""] + params.vocab_size = num_tokens(token_table) + 1 logging.info(params) @@ -709,6 +707,8 @@ def main(): suffix = f"epoch-{params.epoch}" suffix += f"-avg-{params.avg}" + suffix += f"-chunk-{params.chunk_size}" + suffix += f"-left-{params.left_context_frames}" opset_version = 13 diff --git a/egs/librispeech/ASR/zipformer/export-onnx.py b/egs/librispeech/ASR/zipformer/export-onnx.py index f5b01ce719..1bc10c8966 100755 --- a/egs/librispeech/ASR/zipformer/export-onnx.py +++ b/egs/librispeech/ASR/zipformer/export-onnx.py @@ -1,6 +1,6 @@ #!/usr/bin/env python3 # -# Copyright 2023 Xiaomi Corporation (Author: Fangjun Kuang) +# Copyright 2023 Xiaomi Corporation (Author: Fangjun Kuang, Wei Kang) # Copyright 2023 Danqing Fu (danqing.fu@gmail.com) """ @@ -19,7 +19,7 @@ repo=$(basename $repo_url) pushd $repo -git lfs pull --include "data/lang_bpe_500/bpe.model" +git lfs pull --include "data/lang_bpe_500/tokens.txt" git lfs pull --include "exp/pretrained.pt" cd exp @@ -29,12 +29,11 @@ 2. Export the model to ONNX ./zipformer/export-onnx.py \ - --bpe-model $repo/data/lang_bpe_500/bpe.model \ + --tokens $repo/data/lang_bpe_500/tokens.txt \ --use-averaged-model 0 \ --epoch 99 \ --avg 1 \ --exp-dir $repo/exp \ - \ --num-encoder-layers "2,2,3,4,3,2" \ --downsampling-factor "1,2,4,8,4,2" \ --feedforward-dim "512,768,1024,1536,1024,768" \ @@ -67,14 +66,15 @@ from pathlib import Path from typing import Dict, Tuple +import k2 import onnx -import sentencepiece as spm import torch import torch.nn as nn from decoder import Decoder +from export import num_tokens from onnxruntime.quantization import QuantType, quantize_dynamic from scaling_converter import convert_scaled_to_non_scaled -from train import add_model_arguments, get_params, get_model +from train import add_model_arguments, get_model, get_params from zipformer import Zipformer2 from icefall.checkpoint import ( @@ -83,7 +83,7 @@ find_checkpoints, load_checkpoint, ) -from icefall.utils import str2bool, make_pad_mask +from icefall.utils import make_pad_mask, str2bool def get_parser(): @@ -140,10 +140,10 @@ def get_parser(): ) parser.add_argument( - "--bpe-model", + "--tokens", type=str, - default="data/lang_bpe_500/bpe.model", - help="Path to the BPE model", + default="data/lang_bpe_500/tokens.txt", + help="Path to the tokens.txt", ) parser.add_argument( @@ -434,12 +434,9 @@ def main(): logging.info(f"device: {device}") - sp = spm.SentencePieceProcessor() - sp.load(params.bpe_model) - - # is defined in local/train_bpe_model.py - params.blank_id = sp.piece_to_id("") - params.vocab_size = sp.get_piece_size() + token_table = k2.SymbolTable.from_file(params.tokens) + params.blank_id = token_table[""] + params.vocab_size = num_tokens(token_table) + 1 logging.info(params) diff --git a/egs/librispeech/ASR/zipformer/export.py b/egs/librispeech/ASR/zipformer/export.py index a100cbb8dd..4a48d5badb 100755 --- a/egs/librispeech/ASR/zipformer/export.py +++ b/egs/librispeech/ASR/zipformer/export.py @@ -1,6 +1,8 @@ #!/usr/bin/env python3 # -# Copyright 2021-2023 Xiaomi Corporation (Author: Fangjun Kuang, Zengwei Yao) +# Copyright 2021-2023 Xiaomi Corporation (Author: Fangjun Kuang, +# Zengwei Yao, +# Wei Kang) # # See ../../../../LICENSE for clarification regarding multiple authors # @@ -22,13 +24,16 @@ Usage: +Note: This is a example for librispeech dataset, if you are using different +dataset, you should change the argument values according to your dataset. + (1) Export to torchscript model using torch.jit.script() - For non-streaming model: ./zipformer/export.py \ --exp-dir ./zipformer/exp \ - --bpe-model data/lang_bpe_500/bpe.model \ + --tokens data/lang_bpe_500/tokens.txt \ --epoch 30 \ --avg 9 \ --jit 1 @@ -48,7 +53,7 @@ --causal 1 \ --chunk-size 16 \ --left-context-frames 128 \ - --bpe-model data/lang_bpe_500/bpe.model \ + --tokens data/lang_bpe_500/tokens.txt \ --epoch 30 \ --avg 9 \ --jit 1 @@ -67,7 +72,7 @@ ./zipformer/export.py \ --exp-dir ./zipformer/exp \ - --bpe-model data/lang_bpe_500/bpe.model \ + --tokens data/lang_bpe_500/tokens.txt \ --epoch 30 \ --avg 9 @@ -76,7 +81,7 @@ ./zipformer/export.py \ --exp-dir ./zipformer/exp \ --causal 1 \ - --bpe-model data/lang_bpe_500/bpe.model \ + --tokens data/lang_bpe_500/tokens.txt \ --epoch 30 \ --avg 9 @@ -155,13 +160,15 @@ import argparse import logging +import re from pathlib import Path from typing import List, Tuple -import sentencepiece as spm +import k2 import torch +from scaling_converter import convert_scaled_to_non_scaled from torch import Tensor, nn -from train import add_model_arguments, get_params, get_model +from train import add_model_arguments, get_model, get_params from icefall.checkpoint import ( average_checkpoints, @@ -170,7 +177,26 @@ load_checkpoint, ) from icefall.utils import make_pad_mask, str2bool -from scaling_converter import convert_scaled_to_non_scaled + + +def num_tokens( + token_table: k2.SymbolTable, disambig_pattern: str = re.compile(r"^#\d+$") +) -> int: + """Return the number of tokens excluding those from + disambiguation symbols. + + Caution: + 0 is not a token ID so it is excluded from the return value. + """ + symbols = token_table.symbols + ans = [] + for s in symbols: + if not disambig_pattern.match(s): + ans.append(token_table[s]) + num_tokens = len(ans) + if 0 in ans: + num_tokens -= 1 + return num_tokens def get_parser(): @@ -227,10 +253,10 @@ def get_parser(): ) parser.add_argument( - "--bpe-model", + "--tokens", type=str, - default="data/lang_bpe_500/bpe.model", - help="Path to the BPE model", + default="data/lang_bpe_500/tokens.txt", + help="Path to the tokens.txt", ) parser.add_argument( @@ -238,7 +264,7 @@ def get_parser(): type=str2bool, default=False, help="""True to save a model after applying torch.jit.script. - It will generate a file named cpu_jit.pt. + It will generate a file named jit_script.pt. Check ./jit_pretrained.py for how to use it. """, ) @@ -257,6 +283,7 @@ def get_parser(): class EncoderModel(nn.Module): """A wrapper for encoder and encoder_embed""" + def __init__(self, encoder: nn.Module, encoder_embed: nn.Module) -> None: super().__init__() self.encoder = encoder @@ -275,9 +302,7 @@ def forward( src_key_padding_mask = make_pad_mask(x_lens) x = x.permute(1, 0, 2) # (N, T, C) -> (T, N, C) - encoder_out, encoder_out_lens = self.encoder( - x, x_lens, src_key_padding_mask - ) + encoder_out, encoder_out_lens = self.encoder(x, x_lens, src_key_padding_mask) encoder_out = encoder_out.permute(1, 0, 2) # (T, N, C) ->(N, T, C) return encoder_out, encoder_out_lens @@ -398,12 +423,9 @@ def main(): logging.info(f"device: {device}") - sp = spm.SentencePieceProcessor() - sp.load(params.bpe_model) - - # is defined in local/train_bpe_model.py - params.blank_id = sp.piece_to_id("") - params.vocab_size = sp.get_piece_size() + token_table = k2.SymbolTable.from_file(params.tokens) + params.blank_id = token_table[""] + params.vocab_size = num_tokens(token_table) + 1 logging.info(params) diff --git a/egs/librispeech/ASR/zipformer/generate_averaged_model.py b/egs/librispeech/ASR/zipformer/generate_averaged_model.py index e0c7b52cb4..68111fad71 100755 --- a/egs/librispeech/ASR/zipformer/generate_averaged_model.py +++ b/egs/librispeech/ASR/zipformer/generate_averaged_model.py @@ -40,16 +40,11 @@ import argparse from pathlib import Path -import sentencepiece as spm +import k2 import torch -from asr_datamodule import LibriSpeechAsrDataModule +from train import add_model_arguments, get_model, get_params -from train import add_model_arguments, get_params, get_model - -from icefall.checkpoint import ( - average_checkpoints_with_averaged_model, - find_checkpoints, -) +from icefall.checkpoint import average_checkpoints_with_averaged_model, find_checkpoints def get_parser(): @@ -93,10 +88,10 @@ def get_parser(): ) parser.add_argument( - "--bpe-model", + "--tokens", type=str, - default="data/lang_bpe_500/bpe.model", - help="Path to the BPE model", + default="data/lang_bpe_500/tokens.txt", + help="Path to the tokens.txt", ) parser.add_argument( @@ -114,7 +109,6 @@ def get_parser(): @torch.no_grad() def main(): parser = get_parser() - LibriSpeechAsrDataModule.add_arguments(parser) args = parser.parse_args() args.exp_dir = Path(args.exp_dir) @@ -131,13 +125,10 @@ def main(): device = torch.device("cpu") print(f"Device: {device}") - sp = spm.SentencePieceProcessor() - sp.load(params.bpe_model) - - # is defined in local/train_bpe_model.py - params.blank_id = sp.piece_to_id("") - params.unk_id = sp.piece_to_id("") - params.vocab_size = sp.get_piece_size() + symbol_table = k2.SymbolTable.from_file(params.tokens) + params.blank_id = symbol_table[""] + params.unk_id = symbol_table[""] + params.vocab_size = len(symbol_table) print("About to create model") model = get_model(params) diff --git a/egs/librispeech/ASR/zipformer/jit_pretrained.py b/egs/librispeech/ASR/zipformer/jit_pretrained.py index 87cd5102cb..a41fbc1c97 100755 --- a/egs/librispeech/ASR/zipformer/jit_pretrained.py +++ b/egs/librispeech/ASR/zipformer/jit_pretrained.py @@ -21,7 +21,7 @@ ./zipformer/export.py \ --exp-dir ./zipformer/exp \ - --bpe-model data/lang_bpe_500/bpe.model \ + --tokens data/lang_bpe_500/tokens.txt \ --epoch 30 \ --avg 9 \ --jit 1 @@ -30,7 +30,7 @@ ./zipformer/jit_pretrained.py \ --nn-model-filename ./zipformer/exp/cpu_jit.pt \ - --bpe-model ./data/lang_bpe_500/bpe.model \ + --tokens ./data/lang_bpe_500/tokens.txt \ /path/to/foo.wav \ /path/to/bar.wav """ @@ -40,8 +40,8 @@ import math from typing import List +import k2 import kaldifeat -import sentencepiece as spm import torch import torchaudio from torch.nn.utils.rnn import pad_sequence @@ -60,9 +60,9 @@ def get_parser(): ) parser.add_argument( - "--bpe-model", + "--tokens", type=str, - help="""Path to bpe.model.""", + help="""Path to tokens.txt.""", ) parser.add_argument( @@ -128,7 +128,7 @@ def greedy_search( ) device = encoder_out.device - blank_id = 0 # hard-code to 0 + blank_id = model.decoder.blank_id batch_size_list = packed_encoder_out.batch_sizes.tolist() N = encoder_out.size(0) @@ -215,9 +215,6 @@ def main(): model.to(device) - sp = spm.SentencePieceProcessor() - sp.load(args.bpe_model) - logging.info("Constructing Fbank computer") opts = kaldifeat.FbankOptions() opts.device = device @@ -256,10 +253,21 @@ def main(): encoder_out=encoder_out, encoder_out_lens=encoder_out_lens, ) + s = "\n" + + token_table = k2.SymbolTable.from_file(args.tokens) + + def token_ids_to_words(token_ids: List[int]) -> str: + text = "" + for i in token_ids: + text += token_table[i] + return text.replace("▁", " ").strip() + for filename, hyp in zip(args.sound_files, hyps): - words = sp.decode(hyp) - s += f"{filename}:\n{words}\n\n" + words = token_ids_to_words(hyp) + s += f"{filename}:\n{words}\n" + logging.info(s) logging.info("Decoding Done") diff --git a/egs/librispeech/ASR/zipformer/jit_pretrained_ctc.py b/egs/librispeech/ASR/zipformer/jit_pretrained_ctc.py index 1ec390d5b0..14faeedd12 100755 --- a/egs/librispeech/ASR/zipformer/jit_pretrained_ctc.py +++ b/egs/librispeech/ASR/zipformer/jit_pretrained_ctc.py @@ -24,7 +24,7 @@ ./zipformer/export.py \ --exp-dir ./zipformer/exp \ --use-ctc 1 \ - --bpe-model data/lang_bpe_500/bpe.model \ + --tokens data/lang_bpe_500/tokens.txt \ --epoch 30 \ --avg 9 \ --jit 1 @@ -35,7 +35,7 @@ --exp-dir ./zipformer/exp \ --use-ctc 1 \ --causal 1 \ - --bpe-model data/lang_bpe_500/bpe.model \ + --tokens data/lang_bpe_500/tokens.txt \ --epoch 30 \ --avg 9 \ --jit 1 @@ -45,7 +45,7 @@ (1) ctc-decoding ./zipformer/jit_pretrained_ctc.py \ --model-filename ./zipformer/exp/jit_script.pt \ - --bpe-model data/lang_bpe_500/bpe.model \ + --tokens data/lang_bpe_500/tokens.txt \ --method ctc-decoding \ --sample-rate 16000 \ /path/to/foo.wav \ @@ -91,10 +91,10 @@ import k2 import kaldifeat -import sentencepiece as spm import torch import torchaudio from ctc_decode import get_decoding_params +from export import num_tokens from torch.nn.utils.rnn import pad_sequence from train import get_params @@ -136,9 +136,9 @@ def get_parser(): ) parser.add_argument( - "--bpe-model", + "--tokens", type=str, - help="""Path to bpe.model. + help="""Path to tokens.txt. Used only when method is ctc-decoding. """, ) @@ -149,8 +149,8 @@ def get_parser(): default="1best", help="""Decoding method. Possible values are: - (0) ctc-decoding - Use CTC decoding. It uses a sentence - piece model, i.e., lang_dir/bpe.model, to convert + (0) ctc-decoding - Use CTC decoding. It uses a token table, + i.e., lang_dir/token.txt, to convert word pieces to words. It needs neither a lexicon nor an n-gram LM. (1) 1best - Use the best path as decoding output. Only @@ -263,10 +263,8 @@ def main(): params.update(get_decoding_params()) params.update(vars(args)) - sp = spm.SentencePieceProcessor() - sp.load(params.bpe_model) - - params.vocab_size = sp.get_piece_size() + token_table = k2.SymbolTable.from_file(params.tokens) + params.vocab_size = num_tokens(token_table) logging.info(f"{params}") @@ -340,8 +338,7 @@ def main(): lattice=lattice, use_double_scores=params.use_double_scores ) token_ids = get_texts(best_path) - hyps = sp.decode(token_ids) - hyps = [s.split() for s in hyps] + hyps = [[token_table[i] for i in ids] for ids in token_ids] elif params.method in [ "1best", "nbest-rescoring", @@ -415,6 +412,7 @@ def main(): s = "\n" for filename, hyp in zip(params.sound_files, hyps): words = " ".join(hyp) + words = words.replace("▁", " ").strip() s += f"{filename}:\n{words}\n\n" logging.info(s) diff --git a/egs/librispeech/ASR/zipformer/jit_pretrained_streaming.py b/egs/librispeech/ASR/zipformer/jit_pretrained_streaming.py index 58d7366853..d4ceacefd3 100755 --- a/egs/librispeech/ASR/zipformer/jit_pretrained_streaming.py +++ b/egs/librispeech/ASR/zipformer/jit_pretrained_streaming.py @@ -25,7 +25,7 @@ --causal 1 \ --chunk-size 16 \ --left-context-frames 128 \ - --bpe-model data/lang_bpe_500/bpe.model \ + --tokens data/lang_bpe_500/tokens.txt \ --epoch 30 \ --avg 9 \ --jit 1 @@ -34,7 +34,7 @@ ./zipformer/jit_pretrained_streaming.py \ --nn-model-filename ./zipformer/exp-causal/jit_script_chunk_16_left_128.pt \ - --bpe-model ./data/lang_bpe_500/bpe.model \ + --tokens ./data/lang_bpe_500/tokens.txt \ /path/to/foo.wav \ """ @@ -43,8 +43,8 @@ import math from typing import List, Optional +import k2 import kaldifeat -import sentencepiece as spm import torch import torchaudio from kaldifeat import FbankOptions, OnlineFbank, OnlineFeature @@ -60,13 +60,13 @@ def get_parser(): "--nn-model-filename", type=str, required=True, - help="Path to the torchscript model cpu_jit.pt", + help="Path to the torchscript model jit_script.pt", ) parser.add_argument( - "--bpe-model", + "--tokens", type=str, - help="""Path to bpe.model.""", + help="""Path to tokens.txt.""", ) parser.add_argument( @@ -120,8 +120,8 @@ def greedy_search( device: torch.device = torch.device("cpu"), ): assert encoder_out.ndim == 2 - context_size = 2 - blank_id = 0 + context_size = decoder.context_size + blank_id = decoder.blank_id if decoder_out is None: assert hyp is None, hyp @@ -190,8 +190,8 @@ def main(): decoder = model.decoder joiner = model.joiner - sp = spm.SentencePieceProcessor() - sp.load(args.bpe_model) + token_table = k2.SymbolTable.from_file(args.tokens) + context_size = decoder.context_size logging.info("Constructing Fbank computer") online_fbank = create_streaming_feature_extractor(args.sample_rate) @@ -250,9 +250,13 @@ def main(): decoder, joiner, encoder_out.squeeze(0), decoder_out, hyp, device=device ) - context_size = 2 + text = "" + for i in hyp[context_size:]: + text += token_table[i] + text = text.replace("▁", " ").strip() + logging.info(args.sound_file) - logging.info(sp.decode(hyp[context_size:])) + logging.info(text) logging.info("Decoding Done") diff --git a/egs/librispeech/ASR/zipformer/onnx_check.py b/egs/librispeech/ASR/zipformer/onnx_check.py new file mode 100755 index 0000000000..b38b875d02 --- /dev/null +++ b/egs/librispeech/ASR/zipformer/onnx_check.py @@ -0,0 +1,241 @@ +#!/usr/bin/env python3 +# +# Copyright 2022 Xiaomi Corporation (Author: Fangjun Kuang) +# +# See ../../../../LICENSE for clarification regarding multiple authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +""" +This script checks that exported onnx models produce the same output +with the given torchscript model for the same input. + +We use the pre-trained model from +https://huggingface.co/Zengwei/icefall-asr-librispeech-zipformer-2023-05-15 +as an example to show how to use this file. + +1. Download the pre-trained model + +cd egs/librispeech/ASR + +repo_url=https://huggingface.co/Zengwei/icefall-asr-librispeech-zipformer-2023-05-15 +GIT_LFS_SKIP_SMUDGE=1 git clone $repo_url +repo=$(basename $repo_url) + +pushd $repo +git lfs pull --include "data/lang_bpe_500/tokens.txt" +git lfs pull --include "exp/pretrained.pt" + +cd exp +ln -s pretrained.pt epoch-99.pt +popd + +2. Export the model via torchscript (torch.jit.script()) + +./zipformer/export.py \ + --tokens $repo/data/lang_bpe_500/tokens.txt \ + --use-averaged-model 0 \ + --epoch 99 \ + --avg 1 \ + --exp-dir $repo/exp/ \ + --jit 1 + +It will generate the following file in $repo/exp: + - jit_script.pt + +3. Export the model to ONNX + +./zipformer/export-onnx.py \ + --tokens $repo/data/lang_bpe_500/tokens.txt \ + --use-averaged-model 0 \ + --epoch 99 \ + --avg 1 \ + --exp-dir $repo/exp/ + +It will generate the following 3 files inside $repo/exp: + + - encoder-epoch-99-avg-1.onnx + - decoder-epoch-99-avg-1.onnx + - joiner-epoch-99-avg-1.onnx + +4. Run this file + +./zipformer/onnx_check.py \ + --jit-filename $repo/exp/jit_script.pt \ + --onnx-encoder-filename $repo/exp/encoder-epoch-99-avg-1.onnx \ + --onnx-decoder-filename $repo/exp/decoder-epoch-99-avg-1.onnx \ + --onnx-joiner-filename $repo/exp/joiner-epoch-99-avg-1.onnx +""" + +import argparse +import logging + +import torch +from onnx_pretrained import OnnxModel + +from icefall import is_module_available + + +def get_parser(): + parser = argparse.ArgumentParser( + formatter_class=argparse.ArgumentDefaultsHelpFormatter + ) + + parser.add_argument( + "--jit-filename", + required=True, + type=str, + help="Path to the torchscript model", + ) + + parser.add_argument( + "--onnx-encoder-filename", + required=True, + type=str, + help="Path to the onnx encoder model", + ) + + parser.add_argument( + "--onnx-decoder-filename", + required=True, + type=str, + help="Path to the onnx decoder model", + ) + + parser.add_argument( + "--onnx-joiner-filename", + required=True, + type=str, + help="Path to the onnx joiner model", + ) + + return parser + + +def test_encoder( + torch_model: torch.jit.ScriptModule, + onnx_model: OnnxModel, +): + C = 80 + for i in range(3): + N = torch.randint(low=1, high=20, size=(1,)).item() + T = torch.randint(low=30, high=50, size=(1,)).item() + logging.info(f"test_encoder: iter {i}, N={N}, T={T}") + + x = torch.rand(N, T, C) + x_lens = torch.randint(low=30, high=T + 1, size=(N,)) + x_lens[0] = T + + torch_encoder_out, torch_encoder_out_lens = torch_model.encoder(x, x_lens) + torch_encoder_out = torch_model.joiner.encoder_proj(torch_encoder_out) + + onnx_encoder_out, onnx_encoder_out_lens = onnx_model.run_encoder(x, x_lens) + + assert torch.allclose(torch_encoder_out, onnx_encoder_out, atol=1e-05), ( + (torch_encoder_out - onnx_encoder_out).abs().max() + ) + + +def test_decoder( + torch_model: torch.jit.ScriptModule, + onnx_model: OnnxModel, +): + context_size = onnx_model.context_size + vocab_size = onnx_model.vocab_size + for i in range(10): + N = torch.randint(1, 100, size=(1,)).item() + logging.info(f"test_decoder: iter {i}, N={N}") + x = torch.randint( + low=1, + high=vocab_size, + size=(N, context_size), + dtype=torch.int64, + ) + torch_decoder_out = torch_model.decoder(x, need_pad=torch.tensor([False])) + torch_decoder_out = torch_model.joiner.decoder_proj(torch_decoder_out) + torch_decoder_out = torch_decoder_out.squeeze(1) + + onnx_decoder_out = onnx_model.run_decoder(x) + assert torch.allclose(torch_decoder_out, onnx_decoder_out, atol=1e-4), ( + (torch_decoder_out - onnx_decoder_out).abs().max() + ) + + +def test_joiner( + torch_model: torch.jit.ScriptModule, + onnx_model: OnnxModel, +): + encoder_dim = torch_model.joiner.encoder_proj.weight.shape[1] + decoder_dim = torch_model.joiner.decoder_proj.weight.shape[1] + for i in range(10): + N = torch.randint(1, 100, size=(1,)).item() + logging.info(f"test_joiner: iter {i}, N={N}") + encoder_out = torch.rand(N, encoder_dim) + decoder_out = torch.rand(N, decoder_dim) + + projected_encoder_out = torch_model.joiner.encoder_proj(encoder_out) + projected_decoder_out = torch_model.joiner.decoder_proj(decoder_out) + + torch_joiner_out = torch_model.joiner(encoder_out, decoder_out) + onnx_joiner_out = onnx_model.run_joiner( + projected_encoder_out, projected_decoder_out + ) + + assert torch.allclose(torch_joiner_out, onnx_joiner_out, atol=1e-4), ( + (torch_joiner_out - onnx_joiner_out).abs().max() + ) + + +@torch.no_grad() +def main(): + args = get_parser().parse_args() + logging.info(vars(args)) + + torch_model = torch.jit.load(args.jit_filename) + + onnx_model = OnnxModel( + encoder_model_filename=args.onnx_encoder_filename, + decoder_model_filename=args.onnx_decoder_filename, + joiner_model_filename=args.onnx_joiner_filename, + ) + + logging.info("Test encoder") + test_encoder(torch_model, onnx_model) + + logging.info("Test decoder") + test_decoder(torch_model, onnx_model) + + logging.info("Test joiner") + test_joiner(torch_model, onnx_model) + logging.info("Finished checking ONNX models") + + +torch.set_num_threads(1) +torch.set_num_interop_threads(1) + +# See https://github.com/pytorch/pytorch/issues/38342 +# and https://github.com/pytorch/pytorch/issues/33354 +# +# If we don't do this, the delay increases whenever there is +# a new request that changes the actual batch size. +# If you use `py-spy dump --pid --native`, you will +# see a lot of time is spent in re-compiling the torch script model. +torch._C._jit_set_profiling_executor(False) +torch._C._jit_set_profiling_mode(False) +torch._C._set_graph_executor_optimize(False) +if __name__ == "__main__": + torch.manual_seed(20220727) + formatter = "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s" + + logging.basicConfig(format=formatter, level=logging.INFO) + main() diff --git a/egs/librispeech/ASR/zipformer/onnx_pretrained-streaming.py b/egs/librispeech/ASR/zipformer/onnx_pretrained-streaming.py index 273f883dfe..2ce4506a8d 100755 --- a/egs/librispeech/ASR/zipformer/onnx_pretrained-streaming.py +++ b/egs/librispeech/ASR/zipformer/onnx_pretrained-streaming.py @@ -524,11 +524,11 @@ def main(): hyp, ) - symbol_table = k2.SymbolTable.from_file(args.tokens) + token_table = k2.SymbolTable.from_file(args.tokens) text = "" for i in hyp[context_size:]: - text += symbol_table[i] + text += token_table[i] text = text.replace("▁", " ").strip() logging.info(args.sound_file) diff --git a/egs/librispeech/ASR/zipformer/onnx_pretrained.py b/egs/librispeech/ASR/zipformer/onnx_pretrained.py deleted file mode 120000 index 0069288fe1..0000000000 --- a/egs/librispeech/ASR/zipformer/onnx_pretrained.py +++ /dev/null @@ -1 +0,0 @@ -../pruned_transducer_stateless7/onnx_pretrained.py \ No newline at end of file diff --git a/egs/librispeech/ASR/zipformer/onnx_pretrained.py b/egs/librispeech/ASR/zipformer/onnx_pretrained.py new file mode 100755 index 0000000000..b821c4e191 --- /dev/null +++ b/egs/librispeech/ASR/zipformer/onnx_pretrained.py @@ -0,0 +1,419 @@ +#!/usr/bin/env python3 +# Copyright 2022 Xiaomi Corp. (authors: Fangjun Kuang) +# +# See ../../../../LICENSE for clarification regarding multiple authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" +This script loads ONNX models and uses them to decode waves. +You can use the following command to get the exported models: + +We use the pre-trained model from +https://huggingface.co/Zengwei/icefall-asr-librispeech-zipformer-2023-05-15 +as an example to show how to use this file. + +1. Download the pre-trained model + +cd egs/librispeech/ASR + +repo_url=https://huggingface.co/Zengwei/icefall-asr-librispeech-zipformer-2023-05-15 +GIT_LFS_SKIP_SMUDGE=1 git clone $repo_url +repo=$(basename $repo_url) + +pushd $repo +git lfs pull --include "data/lang_bpe_500/tokens.txt" +git lfs pull --include "exp/pretrained.pt" + +cd exp +ln -s pretrained.pt epoch-99.pt +popd + +2. Export the model to ONNX + +./zipformer/export-onnx.py \ + --tokens $repo/data/lang_bpe_500/tokens.txt \ + --use-averaged-model 0 \ + --epoch 99 \ + --avg 1 \ + --exp-dir $repo/exp \ + --causal False + +It will generate the following 3 files inside $repo/exp: + + - encoder-epoch-99-avg-1.onnx + - decoder-epoch-99-avg-1.onnx + - joiner-epoch-99-avg-1.onnx + +3. Run this file + +./pruned_transducer_stateless3/onnx_pretrained.py \ + --encoder-model-filename $repo/exp/encoder-epoch-99-avg-1.onnx \ + --decoder-model-filename $repo/exp/decoder-epoch-99-avg-1.onnx \ + --joiner-model-filename $repo/exp/joiner-epoch-99-avg-1.onnx \ + --tokens $repo/data/lang_bpe_500/tokens.txt \ + $repo/test_wavs/1089-134686-0001.wav \ + $repo/test_wavs/1221-135766-0001.wav \ + $repo/test_wavs/1221-135766-0002.wav +""" + +import argparse +import logging +import math +from typing import List, Tuple + +import k2 +import kaldifeat +import onnxruntime as ort +import torch +import torchaudio +from torch.nn.utils.rnn import pad_sequence + + +def get_parser(): + parser = argparse.ArgumentParser( + formatter_class=argparse.ArgumentDefaultsHelpFormatter + ) + + parser.add_argument( + "--encoder-model-filename", + type=str, + required=True, + help="Path to the encoder onnx model. ", + ) + + parser.add_argument( + "--decoder-model-filename", + type=str, + required=True, + help="Path to the decoder onnx model. ", + ) + + parser.add_argument( + "--joiner-model-filename", + type=str, + required=True, + help="Path to the joiner onnx model. ", + ) + + parser.add_argument( + "--tokens", + type=str, + help="""Path to tokens.txt.""", + ) + + parser.add_argument( + "sound_files", + type=str, + nargs="+", + help="The input sound file(s) to transcribe. " + "Supported formats are those supported by torchaudio.load(). " + "For example, wav and flac are supported. " + "The sample rate has to be 16kHz.", + ) + + parser.add_argument( + "--sample-rate", + type=int, + default=16000, + help="The sample rate of the input sound file", + ) + + return parser + + +class OnnxModel: + def __init__( + self, + encoder_model_filename: str, + decoder_model_filename: str, + joiner_model_filename: str, + ): + session_opts = ort.SessionOptions() + session_opts.inter_op_num_threads = 1 + session_opts.intra_op_num_threads = 4 + + self.session_opts = session_opts + + self.init_encoder(encoder_model_filename) + self.init_decoder(decoder_model_filename) + self.init_joiner(joiner_model_filename) + + def init_encoder(self, encoder_model_filename: str): + self.encoder = ort.InferenceSession( + encoder_model_filename, + sess_options=self.session_opts, + ) + + def init_decoder(self, decoder_model_filename: str): + self.decoder = ort.InferenceSession( + decoder_model_filename, + sess_options=self.session_opts, + ) + + decoder_meta = self.decoder.get_modelmeta().custom_metadata_map + self.context_size = int(decoder_meta["context_size"]) + self.vocab_size = int(decoder_meta["vocab_size"]) + + logging.info(f"context_size: {self.context_size}") + logging.info(f"vocab_size: {self.vocab_size}") + + def init_joiner(self, joiner_model_filename: str): + self.joiner = ort.InferenceSession( + joiner_model_filename, + sess_options=self.session_opts, + ) + + joiner_meta = self.joiner.get_modelmeta().custom_metadata_map + self.joiner_dim = int(joiner_meta["joiner_dim"]) + + logging.info(f"joiner_dim: {self.joiner_dim}") + + def run_encoder( + self, + x: torch.Tensor, + x_lens: torch.Tensor, + ) -> Tuple[torch.Tensor, torch.Tensor]: + """ + Args: + x: + A 3-D tensor of shape (N, T, C) + x_lens: + A 2-D tensor of shape (N,). Its dtype is torch.int64 + Returns: + Return a tuple containing: + - encoder_out, its shape is (N, T', joiner_dim) + - encoder_out_lens, its shape is (N,) + """ + out = self.encoder.run( + [ + self.encoder.get_outputs()[0].name, + self.encoder.get_outputs()[1].name, + ], + { + self.encoder.get_inputs()[0].name: x.numpy(), + self.encoder.get_inputs()[1].name: x_lens.numpy(), + }, + ) + return torch.from_numpy(out[0]), torch.from_numpy(out[1]) + + def run_decoder(self, decoder_input: torch.Tensor) -> torch.Tensor: + """ + Args: + decoder_input: + A 2-D tensor of shape (N, context_size) + Returns: + Return a 2-D tensor of shape (N, joiner_dim) + """ + out = self.decoder.run( + [self.decoder.get_outputs()[0].name], + {self.decoder.get_inputs()[0].name: decoder_input.numpy()}, + )[0] + + return torch.from_numpy(out) + + def run_joiner( + self, encoder_out: torch.Tensor, decoder_out: torch.Tensor + ) -> torch.Tensor: + """ + Args: + encoder_out: + A 2-D tensor of shape (N, joiner_dim) + decoder_out: + A 2-D tensor of shape (N, joiner_dim) + Returns: + Return a 2-D tensor of shape (N, vocab_size) + """ + out = self.joiner.run( + [self.joiner.get_outputs()[0].name], + { + self.joiner.get_inputs()[0].name: encoder_out.numpy(), + self.joiner.get_inputs()[1].name: decoder_out.numpy(), + }, + )[0] + + return torch.from_numpy(out) + + +def read_sound_files( + filenames: List[str], expected_sample_rate: float +) -> List[torch.Tensor]: + """Read a list of sound files into a list 1-D float32 torch tensors. + Args: + filenames: + A list of sound filenames. + expected_sample_rate: + The expected sample rate of the sound files. + Returns: + Return a list of 1-D float32 torch tensors. + """ + ans = [] + for f in filenames: + wave, sample_rate = torchaudio.load(f) + assert ( + sample_rate == expected_sample_rate + ), f"expected sample rate: {expected_sample_rate}. Given: {sample_rate}" + # We use only the first channel + ans.append(wave[0]) + return ans + + +def greedy_search( + model: OnnxModel, + encoder_out: torch.Tensor, + encoder_out_lens: torch.Tensor, +) -> List[List[int]]: + """Greedy search in batch mode. It hardcodes --max-sym-per-frame=1. + Args: + model: + The transducer model. + encoder_out: + A 3-D tensor of shape (N, T, joiner_dim) + encoder_out_lens: + A 1-D tensor of shape (N,). + Returns: + Return the decoded results for each utterance. + """ + assert encoder_out.ndim == 3, encoder_out.shape + assert encoder_out.size(0) >= 1, encoder_out.size(0) + + packed_encoder_out = torch.nn.utils.rnn.pack_padded_sequence( + input=encoder_out, + lengths=encoder_out_lens.cpu(), + batch_first=True, + enforce_sorted=False, + ) + + blank_id = 0 # hard-code to 0 + + batch_size_list = packed_encoder_out.batch_sizes.tolist() + N = encoder_out.size(0) + + assert torch.all(encoder_out_lens > 0), encoder_out_lens + assert N == batch_size_list[0], (N, batch_size_list) + + context_size = model.context_size + hyps = [[blank_id] * context_size for _ in range(N)] + + decoder_input = torch.tensor( + hyps, + dtype=torch.int64, + ) # (N, context_size) + + decoder_out = model.run_decoder(decoder_input) + + offset = 0 + for batch_size in batch_size_list: + start = offset + end = offset + batch_size + current_encoder_out = packed_encoder_out.data[start:end] + # current_encoder_out's shape: (batch_size, joiner_dim) + offset = end + + decoder_out = decoder_out[:batch_size] + logits = model.run_joiner(current_encoder_out, decoder_out) + + # logits'shape (batch_size, vocab_size) + + assert logits.ndim == 2, logits.shape + y = logits.argmax(dim=1).tolist() + emitted = False + for i, v in enumerate(y): + if v != blank_id: + hyps[i].append(v) + emitted = True + if emitted: + # update decoder output + decoder_input = [h[-context_size:] for h in hyps[:batch_size]] + decoder_input = torch.tensor( + decoder_input, + dtype=torch.int64, + ) + decoder_out = model.run_decoder(decoder_input) + + sorted_ans = [h[context_size:] for h in hyps] + ans = [] + unsorted_indices = packed_encoder_out.unsorted_indices.tolist() + for i in range(N): + ans.append(sorted_ans[unsorted_indices[i]]) + + return ans + + +@torch.no_grad() +def main(): + parser = get_parser() + args = parser.parse_args() + logging.info(vars(args)) + model = OnnxModel( + encoder_model_filename=args.encoder_model_filename, + decoder_model_filename=args.decoder_model_filename, + joiner_model_filename=args.joiner_model_filename, + ) + + logging.info("Constructing Fbank computer") + opts = kaldifeat.FbankOptions() + opts.device = "cpu" + opts.frame_opts.dither = 0 + opts.frame_opts.snip_edges = False + opts.frame_opts.samp_freq = args.sample_rate + opts.mel_opts.num_bins = 80 + + fbank = kaldifeat.Fbank(opts) + + logging.info(f"Reading sound files: {args.sound_files}") + waves = read_sound_files( + filenames=args.sound_files, + expected_sample_rate=args.sample_rate, + ) + + logging.info("Decoding started") + features = fbank(waves) + feature_lengths = [f.size(0) for f in features] + + features = pad_sequence( + features, + batch_first=True, + padding_value=math.log(1e-10), + ) + + feature_lengths = torch.tensor(feature_lengths, dtype=torch.int64) + encoder_out, encoder_out_lens = model.run_encoder(features, feature_lengths) + + hyps = greedy_search( + model=model, + encoder_out=encoder_out, + encoder_out_lens=encoder_out_lens, + ) + s = "\n" + + token_table = k2.SymbolTable.from_file(args.tokens) + + def token_ids_to_words(token_ids: List[int]) -> str: + text = "" + for i in token_ids: + text += token_table[i] + return text.replace("▁", " ").strip() + + for filename, hyp in zip(args.sound_files, hyps): + words = token_ids_to_words(hyp) + s += f"{filename}:\n{words}\n" + logging.info(s) + + logging.info("Decoding Done") + + +if __name__ == "__main__": + formatter = "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s" + + logging.basicConfig(format=formatter, level=logging.INFO) + main() diff --git a/egs/librispeech/ASR/zipformer/pretrained.py b/egs/librispeech/ASR/zipformer/pretrained.py index 2944f79e3e..3104b60847 100755 --- a/egs/librispeech/ASR/zipformer/pretrained.py +++ b/egs/librispeech/ASR/zipformer/pretrained.py @@ -18,11 +18,14 @@ This script loads a checkpoint and uses it to decode waves. You can generate the checkpoint with the following command: +Note: This is a example for librispeech dataset, if you are using different +dataset, you should change the argument values according to your dataset. + - For non-streaming model: ./zipformer/export.py \ --exp-dir ./zipformer/exp \ - --bpe-model data/lang_bpe_500/bpe.model \ + --tokens data/lang_bpe_500/tokens.txt \ --epoch 30 \ --avg 9 @@ -31,7 +34,7 @@ ./zipformer/export.py \ --exp-dir ./zipformer/exp \ --causal 1 \ - --bpe-model data/lang_bpe_500/bpe.model \ + --tokens data/lang_bpe_500/tokens.txt \ --epoch 30 \ --avg 9 @@ -42,7 +45,7 @@ (1) greedy search ./zipformer/pretrained.py \ --checkpoint ./zipformer/exp/pretrained.pt \ - --bpe-model ./data/lang_bpe_500/bpe.model \ + --tokens data/lang_bpe_500/tokens.txt \ --method greedy_search \ /path/to/foo.wav \ /path/to/bar.wav @@ -50,7 +53,7 @@ (2) modified beam search ./zipformer/pretrained.py \ --checkpoint ./zipformer/exp/pretrained.pt \ - --bpe-model ./data/lang_bpe_500/bpe.model \ + --tokens ./data/lang_bpe_500/tokens.txt \ --method modified_beam_search \ /path/to/foo.wav \ /path/to/bar.wav @@ -58,7 +61,7 @@ (3) fast beam search ./zipformer/pretrained.py \ --checkpoint ./zipformer/exp/pretrained.pt \ - --bpe-model ./data/lang_bpe_500/bpe.model \ + --tokens ./data/lang_bpe_500/tokens.txt \ --method fast_beam_search \ /path/to/foo.wav \ /path/to/bar.wav @@ -71,7 +74,7 @@ --causal 1 \ --chunk-size 16 \ --left-context-frames 128 \ - --bpe-model ./data/lang_bpe_500/bpe.model \ + --tokens ./data/lang_bpe_500/tokens.txt \ --method greedy_search \ /path/to/foo.wav \ /path/to/bar.wav @@ -82,7 +85,7 @@ --causal 1 \ --chunk-size 16 \ --left-context-frames 128 \ - --bpe-model ./data/lang_bpe_500/bpe.model \ + --tokens ./data/lang_bpe_500/tokens.txt \ --method modified_beam_search \ /path/to/foo.wav \ /path/to/bar.wav @@ -93,7 +96,7 @@ --causal 1 \ --chunk-size 16 \ --left-context-frames 128 \ - --bpe-model ./data/lang_bpe_500/bpe.model \ + --tokens ./data/lang_bpe_500/tokens.txt \ --method fast_beam_search \ /path/to/foo.wav \ /path/to/bar.wav @@ -112,7 +115,6 @@ import k2 import kaldifeat -import sentencepiece as spm import torch import torchaudio from beam_search import ( @@ -120,8 +122,11 @@ greedy_search_batch, modified_beam_search, ) +from export import num_tokens from torch.nn.utils.rnn import pad_sequence -from train import add_model_arguments, get_params, get_model +from train import add_model_arguments, get_model, get_params + +from icefall.utils import make_pad_mask def get_parser(): @@ -139,9 +144,9 @@ def get_parser(): ) parser.add_argument( - "--bpe-model", + "--tokens", type=str, - help="""Path to bpe.model.""", + help="""Path to tokens.txt.""", ) parser.add_argument( @@ -258,13 +263,11 @@ def main(): params.update(vars(args)) - sp = spm.SentencePieceProcessor() - sp.load(params.bpe_model) + token_table = k2.SymbolTable.from_file(params.tokens) - # is defined in local/train_bpe_model.py - params.blank_id = sp.piece_to_id("") - params.unk_id = sp.piece_to_id("") - params.vocab_size = sp.get_piece_size() + params.blank_id = token_table[""] + params.unk_id = token_table[""] + params.vocab_size = num_tokens(token_table) + 1 logging.info(f"{params}") @@ -323,6 +326,12 @@ def main(): msg = f"Using {params.method}" logging.info(msg) + def token_ids_to_words(token_ids: List[int]) -> str: + text = "" + for i in token_ids: + text += token_table[i] + return text.replace("▁", " ").strip() + if params.method == "fast_beam_search": decoding_graph = k2.trivial_graph(params.vocab_size - 1, device=device) hyp_tokens = fast_beam_search_one_best( @@ -334,8 +343,8 @@ def main(): max_contexts=params.max_contexts, max_states=params.max_states, ) - for hyp in sp.decode(hyp_tokens): - hyps.append(hyp.split()) + for hyp in hyp_tokens: + hyps.append(token_ids_to_words(hyp)) elif params.method == "modified_beam_search": hyp_tokens = modified_beam_search( model=model, @@ -344,23 +353,22 @@ def main(): beam=params.beam_size, ) - for hyp in sp.decode(hyp_tokens): - hyps.append(hyp.split()) + for hyp in hyp_tokens: + hyps.append(token_ids_to_words(hyp)) elif params.method == "greedy_search" and params.max_sym_per_frame == 1: hyp_tokens = greedy_search_batch( model=model, encoder_out=encoder_out, encoder_out_lens=encoder_out_lens, ) - for hyp in sp.decode(hyp_tokens): - hyps.append(hyp.split()) + for hyp in hyp_tokens: + hyps.append(token_ids_to_words(hyp)) else: raise ValueError(f"Unsupported method: {params.method}") s = "\n" for filename, hyp in zip(params.sound_files, hyps): - words = " ".join(hyp) - s += f"{filename}:\n{words}\n\n" + s += f"{filename}:\n{hyp}\n\n" logging.info(s) logging.info("Decoding Done") diff --git a/egs/librispeech/ASR/zipformer/pretrained_ctc.py b/egs/librispeech/ASR/zipformer/pretrained_ctc.py index f10d954493..be239e9c38 100755 --- a/egs/librispeech/ASR/zipformer/pretrained_ctc.py +++ b/egs/librispeech/ASR/zipformer/pretrained_ctc.py @@ -24,7 +24,7 @@ ./zipformer/export.py \ --exp-dir ./zipformer/exp \ --use-ctc 1 \ - --bpe-model data/lang_bpe_500/bpe.model \ + --tokens data/lang_bpe_500/tokens.txt \ --epoch 30 \ --avg 9 @@ -34,7 +34,7 @@ --exp-dir ./zipformer/exp \ --use-ctc 1 \ --causal 1 \ - --bpe-model data/lang_bpe_500/bpe.model \ + --tokens data/lang_bpe_500/tokens.txt \ --epoch 30 \ --avg 9 @@ -43,7 +43,7 @@ (1) ctc-decoding ./zipformer/pretrained_ctc.py \ --checkpoint ./zipformer/exp/pretrained.pt \ - --bpe-model data/lang_bpe_500/bpe.model \ + --tokens data/lang_bpe_500/tokens.txt \ --method ctc-decoding \ --sample-rate 16000 \ /path/to/foo.wav \ @@ -90,12 +90,12 @@ import k2 import kaldifeat -import sentencepiece as spm import torch import torchaudio from ctc_decode import get_decoding_params +from export import num_tokens from torch.nn.utils.rnn import pad_sequence -from train import add_model_arguments, get_params, get_model +from train import add_model_arguments, get_model, get_params from icefall.decode import ( get_lattice, @@ -144,9 +144,9 @@ def get_parser(): ) parser.add_argument( - "--bpe-model", + "--tokens", type=str, - help="""Path to bpe.model. + help="""Path to tokens.txt. Used only when method is ctc-decoding. """, ) @@ -157,8 +157,8 @@ def get_parser(): default="1best", help="""Decoding method. Possible values are: - (0) ctc-decoding - Use CTC decoding. It uses a sentence - piece model, i.e., lang_dir/bpe.model, to convert + (0) ctc-decoding - Use CTC decoding. It uses a token table, + i.e., lang_dir/tokens.txt, to convert word pieces to words. It needs neither a lexicon nor an n-gram LM. (1) 1best - Use the best path as decoding output. Only @@ -273,11 +273,10 @@ def main(): params.update(get_decoding_params()) params.update(vars(args)) - sp = spm.SentencePieceProcessor() - sp.load(params.bpe_model) - - params.vocab_size = sp.get_piece_size() - params.blank_id = 0 + token_table = k2.SymbolTable.from_file(params.tokens) + params.vocab_size = num_tokens(token_table) + params.blank_id = token_table[""] + assert params.blank_id == 0 logging.info(f"{params}") @@ -358,8 +357,7 @@ def main(): lattice=lattice, use_double_scores=params.use_double_scores ) token_ids = get_texts(best_path) - hyps = sp.decode(token_ids) - hyps = [s.split() for s in hyps] + hyps = [[token_table[i] for i in ids] for ids in token_ids] elif params.method in [ "1best", "nbest-rescoring", @@ -433,6 +431,7 @@ def main(): s = "\n" for filename, hyp in zip(params.sound_files, hyps): words = " ".join(hyp) + words = words.replace("▁", " ").strip() s += f"{filename}:\n{words}\n\n" logging.info(s) diff --git a/egs/librispeech/ASR/zipformer/streaming_beam_search.py b/egs/librispeech/ASR/zipformer/streaming_beam_search.py index e6e0fb1c84..3c8565b330 100644 --- a/egs/librispeech/ASR/zipformer/streaming_beam_search.py +++ b/egs/librispeech/ASR/zipformer/streaming_beam_search.py @@ -31,6 +31,7 @@ def greedy_search( model: nn.Module, encoder_out: torch.Tensor, streams: List[DecodeStream], + blank_penalty: float = 0.0, ) -> None: """Greedy search in batch mode. It hardcodes --max-sym-per-frame=1. @@ -71,6 +72,9 @@ def greedy_search( # logits'shape (batch_size, vocab_size) logits = logits.squeeze(1).squeeze(1) + if blank_penalty != 0.0: + logits[:, 0] -= blank_penalty + assert logits.ndim == 2, logits.shape y = logits.argmax(dim=1).tolist() emitted = False @@ -97,6 +101,7 @@ def modified_beam_search( encoder_out: torch.Tensor, streams: List[DecodeStream], num_active_paths: int = 4, + blank_penalty: float = 0.0, ) -> None: """Beam search in batch mode with --max-sym-per-frame=1 being hardcoded. @@ -158,6 +163,9 @@ def modified_beam_search( logits = logits.squeeze(1).squeeze(1) + if blank_penalty != 0.0: + logits[:, 0] -= blank_penalty + log_probs = logits.log_softmax(dim=-1) # (num_hyps, vocab_size) log_probs.add_(ys_log_probs) @@ -205,6 +213,7 @@ def fast_beam_search_one_best( beam: float, max_states: int, max_contexts: int, + blank_penalty: float = 0.0, ) -> None: """It limits the maximum number of symbols per frame to 1. @@ -269,6 +278,10 @@ def fast_beam_search_one_best( project_input=False, ) logits = logits.squeeze(1).squeeze(1) + + if blank_penalty != 0.0: + logits[:, 0] -= blank_penalty + log_probs = logits.log_softmax(dim=-1) decoding_streams.advance(log_probs) diff --git a/egs/wenetspeech/ASR/RESULTS.md b/egs/wenetspeech/ASR/RESULTS.md index 658ad4a9bd..1a0e0681f1 100644 --- a/egs/wenetspeech/ASR/RESULTS.md +++ b/egs/wenetspeech/ASR/RESULTS.md @@ -1,5 +1,90 @@ ## Results +### WenetSpeech char-based training results (Non-streaming and streaming) on zipformer model + +This is the [pull request](https://github.com/k2-fsa/icefall/pull/1130) in icefall. + +#### Non-streaming + +Best results (num of params : ~76M): + +Type | Greedy(dev & net & meeting) | Beam search(dev & net & meeting) |   +-- | -- | -- | -- +Non-streaming | 7.36 & 7.65 & 12.43 | 7.32 & 7.61 & 12.35 | --epoch=12 + +The training command: + +``` +./zipformer/train.py \ + --world-size 6 \ + --num-epochs 12 \ + --use-fp16 1 \ + --max-duration 450 \ + --training-subset L \ + --lr-epochs 1.5 \ + --context-size 2 \ + --exp-dir zipformer/exp_L_context_2 \ + --causal 0 \ + --num-workers 8 +``` + +Listed best results for each epoch below: + +Epoch | Greedy search(dev & net & meeting) | Modified beam search(dev & net & meeting) |   +-- | -- | -- | -- +4 | 7.83 & 8.86 &13.73 | 7.75 & 8.81 & 13.67 | avg=1;blank-penalty=2 +5 | 7.75 & 8.46 & 13.38 | 7.68 & 8.41 & 13.27 | avg=1;blank-penalty=2 +6 | 7.72 & 8.19 & 13.16 | 7.62 & 8.14 & 13.06 | avg=1;blank-penalty=2 +7 | 7.59 & 8.08 & 12.97 | 7.53 & 8.01 & 12.87 | avg=2;blank-penalty=2 +8 | 7.68 & 7.87 & 12.96 | 7.61 & 7.81 & 12.88 | avg=1;blank-penalty=2 +9 | 7.57 & 7.77 & 12.87 | 7.5 & 7.71 & 12.77 | avg=1;blank-penalty=2 +10 | 7.45 & 7.7 & 12.69 | 7.39 & 7.63 & 12.59 | avg=2;blank-penalty=2 +11 | 7.35 & 7.67 & 12.46 | 7.31 & 7.63 & 12.43 | avg=3;blank-penalty=2 +12 | 7.36 & 7.65 & 12.43 | 7.32 & 7.61 & 12.35 | avg=4;blank-penalty=2 + +The pre-trained model is available here : https://huggingface.co/pkufool/icefall-asr-zipformer-wenetspeech-20230615 + + +#### Streaming + +Best results (num of params : ~76M): + +Type | Greedy(dev & net & meeting) | Beam search(dev & net & meeting) |   +-- | -- | -- | -- +Streaming | 8.45 & 9.89 & 16.46 | 8.21 & 9.77 & 16.07 | --epoch=12; --chunk-size=16; --left-context-frames=256 +Streaming | 8.0 & 9.0 & 15.11 | 7.84 & 8.94 & 14.92 | --epoch=12; --chunk-size=32; --left-context-frames=256 + +The training command: + +``` +./zipformer/train.py \ + --world-size 8 \ + --num-epochs 12 \ + --use-fp16 1 \ + --max-duration 450 \ + --training-subset L \ + --lr-epochs 1.5 \ + --context-size 2 \ + --exp-dir zipformer/exp_L_causal_context_2 \ + --causal 1 \ + --num-workers 8 +``` + +Best results for each epoch (--chunk-size=16; --left-context-frames=128) + +Epoch | Greedy search(dev & net & meeting) | Modified beam search(dev & net & meeting) |   +-- | -- | -- | -- +6 | 9.14 & 10.75 & 18.15 | 8.79 & 10.54 & 17.64 | avg=1;blank-penalty=1.5 +7 | 9.11 & 10.61 & 17.86 | 8.8 & 10.42 & 17.29 | avg=1;blank-penalty=1.5 +8 | 8.89 & 10.32 & 17.44 | 8.59 & 10.09 & 16.9 | avg=1;blank-penalty=1.5 +9 | 8.86 & 10.11 & 17.35 | 8.55 & 9.87 & 16.76 | avg=1;blank-penalty=1.5 +10 | 8.66 & 10.0 & 16.94 | 8.39 & 9.83 & 16.47 | avg=2;blank-penalty=1.5 +11 | 8.58 & 9.92 & 16.67 | 8.32 & 9.77 & 16.27 | avg=3;blank-penalty=1.5 +12 | 8.45 & 9.89 & 16.46 | 8.21 & 9.77 & 16.07 | avg=4;blank-penalty=1.5 + +The pre-trained model is available here: https://huggingface.co/pkufool/icefall-asr-zipformer-streaming-wenetspeech-20230615 + + ### WenetSpeech char-based training results (offline and streaming) (Pruned Transducer 5) #### 2022-07-22 diff --git a/egs/wenetspeech/ASR/pruned_transducer_stateless2/asr_datamodule.py b/egs/wenetspeech/ASR/pruned_transducer_stateless2/asr_datamodule.py index 7cb2e1048a..746b212ff4 100644 --- a/egs/wenetspeech/ASR/pruned_transducer_stateless2/asr_datamodule.py +++ b/egs/wenetspeech/ASR/pruned_transducer_stateless2/asr_datamodule.py @@ -292,7 +292,7 @@ def train_dataloaders( max_duration=self.args.max_duration, shuffle=self.args.shuffle, num_buckets=self.args.num_buckets, - buffer_size=30000, + buffer_size=300000, drop_last=True, ) else: diff --git a/egs/wenetspeech/ASR/pruned_transducer_stateless5/decode.py b/egs/wenetspeech/ASR/pruned_transducer_stateless5/decode.py index dc431578cd..36b8a4b670 100755 --- a/egs/wenetspeech/ASR/pruned_transducer_stateless5/decode.py +++ b/egs/wenetspeech/ASR/pruned_transducer_stateless5/decode.py @@ -588,7 +588,7 @@ def decode_dataset( results = defaultdict(list) for batch_idx, batch in enumerate(dl): texts = batch["supervisions"]["text"] - texts = [list(str(text)) for text in texts] + texts = [list("".join(text.split())) for text in texts] cut_ids = [cut.id for cut in batch["supervisions"]["cut"]] hyps_dict = decode_one_batch( diff --git a/egs/wenetspeech/ASR/zipformer/__init__.py b/egs/wenetspeech/ASR/zipformer/__init__.py new file mode 100644 index 0000000000..e69de29bb2 diff --git a/egs/wenetspeech/ASR/zipformer/asr_datamodule.py b/egs/wenetspeech/ASR/zipformer/asr_datamodule.py new file mode 120000 index 0000000000..a074d60850 --- /dev/null +++ b/egs/wenetspeech/ASR/zipformer/asr_datamodule.py @@ -0,0 +1 @@ +../pruned_transducer_stateless2/asr_datamodule.py \ No newline at end of file diff --git a/egs/wenetspeech/ASR/zipformer/beam_search.py b/egs/wenetspeech/ASR/zipformer/beam_search.py new file mode 120000 index 0000000000..8554e44ccf --- /dev/null +++ b/egs/wenetspeech/ASR/zipformer/beam_search.py @@ -0,0 +1 @@ +../pruned_transducer_stateless2/beam_search.py \ No newline at end of file diff --git a/egs/wenetspeech/ASR/zipformer/decode.py b/egs/wenetspeech/ASR/zipformer/decode.py new file mode 100755 index 0000000000..0fbc8244b7 --- /dev/null +++ b/egs/wenetspeech/ASR/zipformer/decode.py @@ -0,0 +1,818 @@ +#!/usr/bin/env python3 +# +# Copyright 2021-2022 Xiaomi Corporation (Author: Fangjun Kuang, +# Zengwei Yao +# Mingshuang Luo) +# +# See ../../../../LICENSE for clarification regarding multiple authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" +Usage: +(1) greedy search +./zipformer/decode.py \ + --epoch 35 \ + --avg 15 \ + --exp-dir ./zipformer/exp \ + --lang-dir data/lang_char \ + --max-duration 600 \ + --decoding-method greedy_search + +(2) modified beam search +./zipformer/decode.py \ + --epoch 35 \ + --avg 15 \ + --exp-dir ./zipformer/exp \ + --lang-dir data/lang_char \ + --max-duration 600 \ + --decoding-method modified_beam_search \ + --beam-size 4 + +(3) fast beam search (trivial_graph) +./zipformer/decode.py \ + --epoch 35 \ + --avg 15 \ + --exp-dir ./zipformer/exp \ + --lang-dir data/lang_char \ + --max-duration 600 \ + --decoding-method fast_beam_search \ + --beam 20.0 \ + --max-contexts 8 \ + --max-states 64 + +(4) fast beam search (LG) +./zipformer/decode.py \ + --epoch 30 \ + --avg 15 \ + --exp-dir ./zipformer/exp \ + --lang-dir data/lang_char \ + --max-duration 600 \ + --decoding-method fast_beam_search_LG \ + --beam 20.0 \ + --max-contexts 8 \ + --max-states 64 + +(5) fast beam search (nbest oracle WER) +./zipformer/decode.py \ + --epoch 35 \ + --avg 15 \ + --exp-dir ./zipformer/exp \ + --lang-dir data/lang_char \ + --max-duration 600 \ + --decoding-method fast_beam_search_nbest_oracle \ + --beam 20.0 \ + --max-contexts 8 \ + --max-states 64 \ + --num-paths 200 \ + --nbest-scale 0.5 +""" + + +import argparse +import logging +import math +from collections import defaultdict +from pathlib import Path +from typing import Dict, List, Optional, Tuple + +import k2 +import torch +import torch.nn as nn +from asr_datamodule import WenetSpeechAsrDataModule +from beam_search import ( + beam_search, + fast_beam_search_nbest, + fast_beam_search_nbest_LG, + fast_beam_search_nbest_oracle, + fast_beam_search_one_best, + greedy_search, + greedy_search_batch, + modified_beam_search, +) +from lhotse.cut import Cut +from train import add_model_arguments, get_model, get_params + +from icefall.char_graph_compiler import CharCtcTrainingGraphCompiler +from icefall.checkpoint import ( + average_checkpoints, + average_checkpoints_with_averaged_model, + find_checkpoints, + load_checkpoint, +) +from icefall.lexicon import Lexicon +from icefall.utils import ( + AttributeDict, + make_pad_mask, + setup_logger, + store_transcripts, + str2bool, + write_error_stats, +) + +LOG_EPS = math.log(1e-10) + + +def get_parser(): + parser = argparse.ArgumentParser( + formatter_class=argparse.ArgumentDefaultsHelpFormatter + ) + + parser.add_argument( + "--epoch", + type=int, + default=30, + help="""It specifies the checkpoint to use for decoding. + Note: Epoch counts from 1. + You can specify --avg to use more checkpoints for model averaging.""", + ) + + parser.add_argument( + "--iter", + type=int, + default=0, + help="""If positive, --epoch is ignored and it + will use the checkpoint exp_dir/checkpoint-iter.pt. + You can specify --avg to use more checkpoints for model averaging. + """, + ) + + parser.add_argument( + "--avg", + type=int, + default=15, + help="Number of checkpoints to average. Automatically select " + "consecutive checkpoints before the checkpoint specified by " + "'--epoch' and '--iter'", + ) + + parser.add_argument( + "--use-averaged-model", + type=str2bool, + default=True, + help="Whether to load averaged model. Currently it only supports " + "using --epoch. If True, it would decode with the averaged model " + "over the epoch range from `epoch-avg` (excluded) to `epoch`." + "Actually only the models with epoch number of `epoch-avg` and " + "`epoch` are loaded for averaging. ", + ) + + parser.add_argument( + "--exp-dir", + type=str, + default="zipformer/exp", + help="The experiment dir", + ) + + parser.add_argument( + "--lang-dir", + type=Path, + default="data/lang_char", + help="The lang dir containing word table and LG graph", + ) + + parser.add_argument( + "--decoding-method", + type=str, + default="greedy_search", + help="""Possible values are: + - greedy_search + - modified_beam_search + - fast_beam_search + - fast_beam_search_LG + - fast_beam_search_nbest_oracle + If you use fast_beam_search_LG, you have to specify + `--lang-dir`, which should contain `LG.pt`. + """, + ) + + parser.add_argument( + "--beam-size", + type=int, + default=4, + help="""An integer indicating how many candidates we will keep for each + frame. Used only when --decoding-method is beam_search or + modified_beam_search.""", + ) + + parser.add_argument( + "--beam", + type=float, + default=20.0, + help="""A floating point value to calculate the cutoff score during beam + search (i.e., `cutoff = max-score - beam`), which is the same as the + `beam` in Kaldi. + Used only when --decoding-method is fast_beam_search, + fast_beam_search, fast_beam_search_LG, + and fast_beam_search_nbest_oracle + """, + ) + + parser.add_argument( + "--ngram-lm-scale", + type=float, + default=0.01, + help=""" + Used only when --decoding_method is fast_beam_search_LG. + It specifies the scale for n-gram LM scores. + """, + ) + + parser.add_argument( + "--ilme-scale", + type=float, + default=0.2, + help=""" + Used only when --decoding_method is fast_beam_search_LG. + It specifies the scale for the internal language model estimation. + """, + ) + + parser.add_argument( + "--max-contexts", + type=int, + default=8, + help="""Used only when --decoding-method is + fast_beam_search, fast_beam_search, fast_beam_search_LG, + and fast_beam_search_nbest_oracle""", + ) + + parser.add_argument( + "--max-states", + type=int, + default=64, + help="""Used only when --decoding-method is + fast_beam_search, fast_beam_search, fast_beam_search_LG, + and fast_beam_search_nbest_oracle""", + ) + + parser.add_argument( + "--context-size", + type=int, + default=2, + help="The context size in the decoder. 1 means bigram; 2 means tri-gram", + ) + + parser.add_argument( + "--max-sym-per-frame", + type=int, + default=1, + help="""Maximum number of symbols per frame. + Used only when --decoding_method is greedy_search""", + ) + + parser.add_argument( + "--num-paths", + type=int, + default=200, + help="""Number of paths for nbest decoding. + Used only when the decoding method is fast_beam_search_nbest_oracle""", + ) + + parser.add_argument( + "--nbest-scale", + type=float, + default=0.5, + help="""Scale applied to lattice scores when computing nbest paths. + Used only when the decoding method is and fast_beam_search_nbest_oracle""", + ) + + parser.add_argument( + "--blank-penalty", + type=float, + default=0.0, + help=""" + The penalty applied on blank symbol during decoding. + Note: It is a positive value that would be applied to logits like + this `logits[:, 0] -= blank_penalty` (suppose logits.shape is + [batch_size, vocab] and blank id is 0). + """, + ) + + add_model_arguments(parser) + + return parser + + +def decode_one_batch( + params: AttributeDict, + model: nn.Module, + lexicon: Lexicon, + graph_compiler: CharCtcTrainingGraphCompiler, + batch: dict, + decoding_graph: Optional[k2.Fsa] = None, +) -> Dict[str, List[List[str]]]: + """Decode one batch and return the result in a dict. The dict has the + following format: + + - key: It indicates the setting used for decoding. For example, + if greedy_search is used, it would be "greedy_search" + If beam search with a beam size of 7 is used, it would be + "beam_7" + - value: It contains the decoding result. `len(value)` equals to + batch size. `value[i]` is the decoding result for the i-th + utterance in the given batch. + Args: + params: + It's the return value of :func:`get_params`. + model: + The neural model. + batch: + It is the return value from iterating + `lhotse.dataset.K2SpeechRecognitionDataset`. See its documentation + for the format of the `batch`. + decoding_graph: + The decoding graph. Can be either a `k2.trivial_graph` or LG, Used + only when --decoding_method is fast_beam_search, fast_beam_search_nbest, + fast_beam_search_nbest_oracle, and fast_beam_search_nbest_LG. + Returns: + Return the decoding result. See above description for the format of + the returned dict. + """ + device = next(model.parameters()).device + feature = batch["inputs"] + assert feature.ndim == 3 + + feature = feature.to(device) + # at entry, feature is (N, T, C) + + supervisions = batch["supervisions"] + feature_lens = supervisions["num_frames"].to(device) + + if params.causal: + # this seems to cause insertions at the end of the utterance if used with zipformer. + pad_len = 30 + feature_lens += pad_len + feature = torch.nn.functional.pad( + feature, + pad=(0, 0, 0, pad_len), + value=LOG_EPS, + ) + + x, x_lens = model.encoder_embed(feature, feature_lens) + + src_key_padding_mask = make_pad_mask(x_lens) + x = x.permute(1, 0, 2) # (N, T, C) -> (T, N, C) + + encoder_out, encoder_out_lens = model.encoder(x, x_lens, src_key_padding_mask) + encoder_out = encoder_out.permute(1, 0, 2) # (T, N, C) ->(N, T, C) + + hyps = [] + + if params.decoding_method == "fast_beam_search": + hyp_tokens = fast_beam_search_one_best( + model=model, + decoding_graph=decoding_graph, + encoder_out=encoder_out, + encoder_out_lens=encoder_out_lens, + beam=params.beam, + max_contexts=params.max_contexts, + max_states=params.max_states, + blank_penalty=params.blank_penalty, + ) + for i in range(encoder_out.size(0)): + hyps.append([lexicon.token_table[idx] for idx in hyp_tokens[i]]) + elif params.decoding_method == "fast_beam_search_LG": + hyp_tokens = fast_beam_search_one_best( + model=model, + decoding_graph=decoding_graph, + encoder_out=encoder_out, + encoder_out_lens=encoder_out_lens, + beam=params.beam, + max_contexts=params.max_contexts, + max_states=params.max_states, + blank_penalty=params.blank_penalty, + ilme_scale=params.ilme_scale, + ) + for hyp in hyp_tokens: + sentence = "".join([lexicon.word_table[i] for i in hyp]) + hyps.append(list(sentence)) + elif params.decoding_method == "fast_beam_search_nbest_oracle": + hyp_tokens = fast_beam_search_nbest_oracle( + model=model, + decoding_graph=decoding_graph, + encoder_out=encoder_out, + encoder_out_lens=encoder_out_lens, + beam=params.beam, + max_contexts=params.max_contexts, + max_states=params.max_states, + num_paths=params.num_paths, + ref_texts=graph_compiler.texts_to_ids(supervisions["text"]), + nbest_scale=params.nbest_scale, + blank_penalty=params.blank_penalty, + ) + for i in range(encoder_out.size(0)): + hyps.append([lexicon.token_table[idx] for idx in hyp_tokens[i]]) + elif params.decoding_method == "greedy_search" and params.max_sym_per_frame == 1: + hyp_tokens = greedy_search_batch( + model=model, + encoder_out=encoder_out, + encoder_out_lens=encoder_out_lens, + blank_penalty=params.blank_penalty, + ) + for i in range(encoder_out.size(0)): + hyps.append([lexicon.token_table[idx] for idx in hyp_tokens[i]]) + elif params.decoding_method == "modified_beam_search": + hyp_tokens = modified_beam_search( + model=model, + encoder_out=encoder_out, + encoder_out_lens=encoder_out_lens, + blank_penalty=params.blank_penalty, + beam=params.beam_size, + ) + for i in range(encoder_out.size(0)): + hyps.append([lexicon.token_table[idx] for idx in hyp_tokens[i]]) + else: + batch_size = encoder_out.size(0) + + for i in range(batch_size): + # fmt: off + encoder_out_i = encoder_out[i:i + 1, :encoder_out_lens[i]] + # fmt: on + if params.decoding_method == "greedy_search": + hyp = greedy_search( + model=model, + encoder_out=encoder_out_i, + max_sym_per_frame=params.max_sym_per_frame, + blank_penalty=params.blank_penalty, + ) + elif params.decoding_method == "beam_search": + hyp = beam_search( + model=model, + encoder_out=encoder_out_i, + beam=params.beam_size, + blank_penalty=params.blank_penalty, + ) + else: + raise ValueError( + f"Unsupported decoding method: {params.decoding_method}" + ) + hyps.append([lexicon.token_table[idx] for idx in hyp]) + + key = f"blank_penalty_{params.blank_penalty}" + if params.decoding_method == "greedy_search": + return {"greedy_search_" + key: hyps} + elif "fast_beam_search" in params.decoding_method: + key += f"_beam_{params.beam}_" + key += f"max_contexts_{params.max_contexts}_" + key += f"max_states_{params.max_states}" + if "nbest" in params.decoding_method: + key += f"_num_paths_{params.num_paths}_" + key += f"nbest_scale_{params.nbest_scale}" + if "LG" in params.decoding_method: + key += f"_ilme_scale_{params.ilme_scale}" + key += f"_ngram_lm_scale_{params.ngram_lm_scale}" + + return {key: hyps} + else: + return {f"beam_size_{params.beam_size}_" + key: hyps} + + +def decode_dataset( + dl: torch.utils.data.DataLoader, + params: AttributeDict, + model: nn.Module, + lexicon: Lexicon, + graph_compiler: CharCtcTrainingGraphCompiler, + decoding_graph: Optional[k2.Fsa] = None, +) -> Dict[str, List[Tuple[List[str], List[str]]]]: + """Decode dataset. + + Args: + dl: + PyTorch's dataloader containing the dataset to decode. + params: + It is returned by :func:`get_params`. + model: + The neural model. + decoding_graph: + The decoding graph. Can be either a `k2.trivial_graph` or LG, Used + only when --decoding_method is fast_beam_search, fast_beam_search_nbest, + fast_beam_search_nbest_oracle, and fast_beam_search_nbest_LG. + Returns: + Return a dict, whose key may be "greedy_search" if greedy search + is used, or it may be "beam_7" if beam size of 7 is used. + Its value is a list of tuples. Each tuple contains two elements: + The first is the reference transcript, and the second is the + predicted result. + """ + num_cuts = 0 + + try: + num_batches = len(dl) + except TypeError: + num_batches = "?" + + if params.decoding_method == "greedy_search": + log_interval = 50 + else: + log_interval = 20 + + results = defaultdict(list) + for batch_idx, batch in enumerate(dl): + texts = batch["supervisions"]["text"] + texts = [list("".join(text.split())) for text in texts] + cut_ids = [cut.id for cut in batch["supervisions"]["cut"]] + + hyps_dict = decode_one_batch( + params=params, + model=model, + lexicon=lexicon, + graph_compiler=graph_compiler, + decoding_graph=decoding_graph, + batch=batch, + ) + + for name, hyps in hyps_dict.items(): + this_batch = [] + assert len(hyps) == len(texts) + for cut_id, hyp_words, ref_text in zip(cut_ids, hyps, texts): + this_batch.append((cut_id, ref_text, hyp_words)) + + results[name].extend(this_batch) + + num_cuts += len(texts) + + if batch_idx % log_interval == 0: + batch_str = f"{batch_idx}/{num_batches}" + + logging.info(f"batch {batch_str}, cuts processed until now is {num_cuts}") + return results + + +def save_results( + params: AttributeDict, + test_set_name: str, + results_dict: Dict[str, List[Tuple[List[int], List[int]]]], +): + test_set_wers = dict() + for key, results in results_dict.items(): + recog_path = ( + params.res_dir / f"recogs-{test_set_name}-{key}-{params.suffix}.txt" + ) + results = sorted(results) + store_transcripts(filename=recog_path, texts=results) + logging.info(f"The transcripts are stored in {recog_path}") + + # The following prints out WERs, per-word error statistics and aligned + # ref/hyp pairs. + errs_filename = ( + params.res_dir / f"errs-{test_set_name}-{key}-{params.suffix}.txt" + ) + with open(errs_filename, "w") as f: + wer = write_error_stats( + f, f"{test_set_name}-{key}", results, enable_log=True + ) + test_set_wers[key] = wer + + logging.info("Wrote detailed error stats to {}".format(errs_filename)) + + test_set_wers = sorted(test_set_wers.items(), key=lambda x: x[1]) + errs_info = ( + params.res_dir / f"wer-summary-{test_set_name}-{key}-{params.suffix}.txt" + ) + with open(errs_info, "w") as f: + print("settings\tWER", file=f) + for key, val in test_set_wers: + print("{}\t{}".format(key, val), file=f) + + s = "\nFor {}, WER of different settings are:\n".format(test_set_name) + note = "\tbest for {}".format(test_set_name) + for key, val in test_set_wers: + s += "{}\t{}{}\n".format(key, val, note) + note = "" + logging.info(s) + + +@torch.no_grad() +def main(): + parser = get_parser() + WenetSpeechAsrDataModule.add_arguments(parser) + args = parser.parse_args() + args.exp_dir = Path(args.exp_dir) + + params = get_params() + params.update(vars(args)) + + assert params.decoding_method in ( + "greedy_search", + "beam_search", + "modified_beam_search", + "fast_beam_search", + "fast_beam_search_LG", + "fast_beam_search_nbest_oracle", + ) + params.res_dir = params.exp_dir / params.decoding_method + + if params.iter > 0: + params.suffix = f"iter-{params.iter}-avg-{params.avg}" + else: + params.suffix = f"epoch-{params.epoch}-avg-{params.avg}" + + if params.causal: + assert ( + "," not in params.chunk_size + ), "chunk_size should be one value in decoding." + assert ( + "," not in params.left_context_frames + ), "left_context_frames should be one value in decoding." + params.suffix += f"-chunk-{params.chunk_size}" + params.suffix += f"-left-context-{params.left_context_frames}" + + if "fast_beam_search" in params.decoding_method: + params.suffix += f"-beam-{params.beam}" + params.suffix += f"-max-contexts-{params.max_contexts}" + params.suffix += f"-max-states-{params.max_states}" + if "nbest" in params.decoding_method: + params.suffix += f"-nbest-scale-{params.nbest_scale}" + params.suffix += f"-num-paths-{params.num_paths}" + if "LG" in params.decoding_method: + params.suffix += f"_ilme_scale_{params.ilme_scale}" + params.suffix += f"-ngram-lm-scale-{params.ngram_lm_scale}" + elif "beam_search" in params.decoding_method: + params.suffix += f"-{params.decoding_method}-beam-size-{params.beam_size}" + else: + params.suffix += f"-context-{params.context_size}" + params.suffix += f"-max-sym-per-frame-{params.max_sym_per_frame}" + params.suffix += f"-blank-penalty-{params.blank_penalty}" + + if params.use_averaged_model: + params.suffix += "-use-averaged-model" + + setup_logger(f"{params.res_dir}/log-decode-{params.suffix}") + logging.info("Decoding started") + + device = torch.device("cpu") + if torch.cuda.is_available(): + device = torch.device("cuda", 0) + + logging.info(f"Device: {device}") + + lexicon = Lexicon(params.lang_dir) + params.blank_id = lexicon.token_table[""] + params.vocab_size = max(lexicon.tokens) + 1 + + graph_compiler = CharCtcTrainingGraphCompiler( + lexicon=lexicon, + device=device, + ) + + logging.info(params) + + logging.info("About to create model") + model = get_model(params) + + if not params.use_averaged_model: + if params.iter > 0: + filenames = find_checkpoints(params.exp_dir, iteration=-params.iter)[ + : params.avg + ] + if len(filenames) == 0: + raise ValueError( + f"No checkpoints found for" + f" --iter {params.iter}, --avg {params.avg}" + ) + elif len(filenames) < params.avg: + raise ValueError( + f"Not enough checkpoints ({len(filenames)}) found for" + f" --iter {params.iter}, --avg {params.avg}" + ) + logging.info(f"averaging {filenames}") + model.to(device) + model.load_state_dict(average_checkpoints(filenames, device=device)) + elif params.avg == 1: + load_checkpoint(f"{params.exp_dir}/epoch-{params.epoch}.pt", model) + else: + start = params.epoch - params.avg + 1 + filenames = [] + for i in range(start, params.epoch + 1): + if i >= 1: + filenames.append(f"{params.exp_dir}/epoch-{i}.pt") + logging.info(f"averaging {filenames}") + model.to(device) + model.load_state_dict(average_checkpoints(filenames, device=device)) + else: + if params.iter > 0: + filenames = find_checkpoints(params.exp_dir, iteration=-params.iter)[ + : params.avg + 1 + ] + if len(filenames) == 0: + raise ValueError( + f"No checkpoints found for" + f" --iter {params.iter}, --avg {params.avg}" + ) + elif len(filenames) < params.avg + 1: + raise ValueError( + f"Not enough checkpoints ({len(filenames)}) found for" + f" --iter {params.iter}, --avg {params.avg}" + ) + filename_start = filenames[-1] + filename_end = filenames[0] + logging.info( + "Calculating the averaged model over iteration checkpoints" + f" from {filename_start} (excluded) to {filename_end}" + ) + model.to(device) + model.load_state_dict( + average_checkpoints_with_averaged_model( + filename_start=filename_start, + filename_end=filename_end, + device=device, + ) + ) + else: + assert params.avg > 0, params.avg + start = params.epoch - params.avg + assert start >= 1, start + filename_start = f"{params.exp_dir}/epoch-{start}.pt" + filename_end = f"{params.exp_dir}/epoch-{params.epoch}.pt" + logging.info( + f"Calculating the averaged model over epoch range from " + f"{start} (excluded) to {params.epoch}" + ) + model.to(device) + model.load_state_dict( + average_checkpoints_with_averaged_model( + filename_start=filename_start, + filename_end=filename_end, + device=device, + ) + ) + + model.to(device) + model.eval() + + if "fast_beam_search" in params.decoding_method: + if "LG" in params.decoding_method: + lexicon = Lexicon(params.lang_dir) + lg_filename = params.lang_dir / "LG.pt" + logging.info(f"Loading {lg_filename}") + decoding_graph = k2.Fsa.from_dict( + torch.load(lg_filename, map_location=device) + ) + decoding_graph.scores *= params.ngram_lm_scale + else: + decoding_graph = k2.trivial_graph(params.vocab_size - 1, device=device) + else: + decoding_graph = None + + num_param = sum([p.numel() for p in model.parameters()]) + logging.info(f"Number of model parameters: {num_param}") + + # we need cut ids to display recognition results. + args.return_cuts = True + wenetspeech = WenetSpeechAsrDataModule(args) + + def remove_short_utt(c: Cut): + T = ((c.num_frames - 7) // 2 + 1) // 2 + if T <= 0: + logging.warning( + f"Exclude cut with ID {c.id} from decoding, num_frames : {c.num_frames}." + ) + return T > 0 + + dev_cuts = wenetspeech.valid_cuts() + dev_cuts = dev_cuts.filter(remove_short_utt) + dev_dl = wenetspeech.valid_dataloaders(dev_cuts) + + test_net_cuts = wenetspeech.test_net_cuts() + test_net_cuts = test_net_cuts.filter(remove_short_utt) + test_net_dl = wenetspeech.test_dataloaders(test_net_cuts) + + test_meeting_cuts = wenetspeech.test_meeting_cuts() + test_meeting_cuts = test_meeting_cuts.filter(remove_short_utt) + test_meeting_dl = wenetspeech.test_dataloaders(test_meeting_cuts) + + test_sets = ["DEV", "TEST_NET", "TEST_MEETING"] + test_dls = [dev_dl, test_net_dl, test_meeting_dl] + + for test_set, test_dl in zip(test_sets, test_dls): + results_dict = decode_dataset( + dl=test_dl, + params=params, + model=model, + lexicon=lexicon, + graph_compiler=graph_compiler, + decoding_graph=decoding_graph, + ) + + save_results( + params=params, + test_set_name=test_set, + results_dict=results_dict, + ) + + logging.info("Done!") + + +if __name__ == "__main__": + main() diff --git a/egs/wenetspeech/ASR/zipformer/decode_stream.py b/egs/wenetspeech/ASR/zipformer/decode_stream.py new file mode 120000 index 0000000000..b8d8ddfc4c --- /dev/null +++ b/egs/wenetspeech/ASR/zipformer/decode_stream.py @@ -0,0 +1 @@ +../../../librispeech/ASR/zipformer/decode_stream.py \ No newline at end of file diff --git a/egs/wenetspeech/ASR/zipformer/decoder.py b/egs/wenetspeech/ASR/zipformer/decoder.py new file mode 120000 index 0000000000..5a8018680d --- /dev/null +++ b/egs/wenetspeech/ASR/zipformer/decoder.py @@ -0,0 +1 @@ +../../../librispeech/ASR/zipformer/decoder.py \ No newline at end of file diff --git a/egs/wenetspeech/ASR/zipformer/encoder_interface.py b/egs/wenetspeech/ASR/zipformer/encoder_interface.py new file mode 120000 index 0000000000..b9aa0ae083 --- /dev/null +++ b/egs/wenetspeech/ASR/zipformer/encoder_interface.py @@ -0,0 +1 @@ +../pruned_transducer_stateless2/encoder_interface.py \ No newline at end of file diff --git a/egs/wenetspeech/ASR/zipformer/export-onnx-streaming.py b/egs/wenetspeech/ASR/zipformer/export-onnx-streaming.py new file mode 120000 index 0000000000..2962eb7847 --- /dev/null +++ b/egs/wenetspeech/ASR/zipformer/export-onnx-streaming.py @@ -0,0 +1 @@ +../../../librispeech/ASR/zipformer/export-onnx-streaming.py \ No newline at end of file diff --git a/egs/wenetspeech/ASR/zipformer/export-onnx.py b/egs/wenetspeech/ASR/zipformer/export-onnx.py new file mode 120000 index 0000000000..70a15683c2 --- /dev/null +++ b/egs/wenetspeech/ASR/zipformer/export-onnx.py @@ -0,0 +1 @@ +../../../librispeech/ASR/zipformer/export-onnx.py \ No newline at end of file diff --git a/egs/wenetspeech/ASR/zipformer/export.py b/egs/wenetspeech/ASR/zipformer/export.py new file mode 120000 index 0000000000..dfc1bec080 --- /dev/null +++ b/egs/wenetspeech/ASR/zipformer/export.py @@ -0,0 +1 @@ +../../../librispeech/ASR/zipformer/export.py \ No newline at end of file diff --git a/egs/wenetspeech/ASR/zipformer/jit_pretrained.py b/egs/wenetspeech/ASR/zipformer/jit_pretrained.py new file mode 120000 index 0000000000..25108391fa --- /dev/null +++ b/egs/wenetspeech/ASR/zipformer/jit_pretrained.py @@ -0,0 +1 @@ +../../../librispeech/ASR/zipformer/jit_pretrained.py \ No newline at end of file diff --git a/egs/wenetspeech/ASR/zipformer/jit_pretrained_streaming.py b/egs/wenetspeech/ASR/zipformer/jit_pretrained_streaming.py new file mode 120000 index 0000000000..1962351e9a --- /dev/null +++ b/egs/wenetspeech/ASR/zipformer/jit_pretrained_streaming.py @@ -0,0 +1 @@ +../../../librispeech/ASR/zipformer/jit_pretrained_streaming.py \ No newline at end of file diff --git a/egs/wenetspeech/ASR/zipformer/joiner.py b/egs/wenetspeech/ASR/zipformer/joiner.py new file mode 120000 index 0000000000..5b8a36332e --- /dev/null +++ b/egs/wenetspeech/ASR/zipformer/joiner.py @@ -0,0 +1 @@ +../../../librispeech/ASR/zipformer/joiner.py \ No newline at end of file diff --git a/egs/wenetspeech/ASR/zipformer/model.py b/egs/wenetspeech/ASR/zipformer/model.py new file mode 120000 index 0000000000..cd7e07d72b --- /dev/null +++ b/egs/wenetspeech/ASR/zipformer/model.py @@ -0,0 +1 @@ +../../../librispeech/ASR/zipformer/model.py \ No newline at end of file diff --git a/egs/wenetspeech/ASR/zipformer/onnx_check.py b/egs/wenetspeech/ASR/zipformer/onnx_check.py new file mode 120000 index 0000000000..f3dd420046 --- /dev/null +++ b/egs/wenetspeech/ASR/zipformer/onnx_check.py @@ -0,0 +1 @@ +../../../librispeech/ASR/zipformer/onnx_check.py \ No newline at end of file diff --git a/egs/wenetspeech/ASR/zipformer/onnx_decode.py b/egs/wenetspeech/ASR/zipformer/onnx_decode.py new file mode 100755 index 0000000000..ed5f6db08c --- /dev/null +++ b/egs/wenetspeech/ASR/zipformer/onnx_decode.py @@ -0,0 +1,334 @@ +#!/usr/bin/env python3 +# +# Copyright 2021-2023 Xiaomi Corporation (Author: Fangjun Kuang, +# Zengwei Yao, +# Xiaoyu Yang, +# Wei Kang) +# +# See ../../../../LICENSE for clarification regarding multiple authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" +This script loads ONNX exported models and uses them to decode the test sets. + +We use the pre-trained model from +https://huggingface.co/pkufool/icefall-asr-zipformer-wenetspeech-20230615 +as an example to show how to use this file. + +1. Download the pre-trained model + +cd egs/wenetspeech/ASR + +repo_url=https://huggingface.co/pkufool/icefall-asr-zipformer-wenetspeech-20230615 +GIT_LFS_SKIP_SMUDGE=1 git clone $repo_url +repo=$(basename $repo_url) + +pushd $repo +git lfs pull --include "data/lang_char/tokens.txt" +git lfs pull --include "exp/pretrained.pt" + +cd exp +ln -s pretrained.pt epoch-9999.pt +popd + +2. Export the model to ONNX + +./zipformer/export-onnx.py \ + --tokens $repo/data/lang_char/tokens.txt \ + --epoch 9999 \ + --avg 1 \ + --exp-dir $repo/exp/ + +It will generate the following 3 files inside $repo/exp: + + - encoder-epoch-9999-avg-1.onnx + - decoder-epoch-9999-avg-1.onnx + - joiner-epoch-9999-avg-1.onnx + +2. Run this file + +./zipformer/onnx_decode.py \ + --exp-dir ./zipformer/exp \ + --max-duration 600 \ + --encoder-model-filename $repo/exp/encoder-epoch-9999-avg-1.onnx \ + --decoder-model-filename $repo/exp/decoder-epoch-9999-avg-1.onnx \ + --joiner-model-filename $repo/exp/joiner-epoch-9999-avg-1.onnx \ +""" + + +import argparse +import logging +import time +from pathlib import Path +from typing import List, Tuple + +import k2 +import torch +import torch.nn as nn +from asr_datamodule import WenetSpeechAsrDataModule +from lhotse.cut import Cut +from onnx_pretrained import OnnxModel, greedy_search + +from icefall.utils import setup_logger, store_transcripts, write_error_stats + + +def get_parser(): + parser = argparse.ArgumentParser( + formatter_class=argparse.ArgumentDefaultsHelpFormatter + ) + + parser.add_argument( + "--encoder-model-filename", + type=str, + required=True, + help="Path to the encoder onnx model. ", + ) + + parser.add_argument( + "--decoder-model-filename", + type=str, + required=True, + help="Path to the decoder onnx model. ", + ) + + parser.add_argument( + "--joiner-model-filename", + type=str, + required=True, + help="Path to the joiner onnx model. ", + ) + + parser.add_argument( + "--exp-dir", + type=str, + default="pruned_transducer_stateless7/exp", + help="The experiment dir", + ) + + parser.add_argument( + "--tokens", + type=str, + default="data/lang_char/tokens.txt", + help="Path to the tokens.txt", + ) + + parser.add_argument( + "--decoding-method", + type=str, + default="greedy_search", + help="Valid values are greedy_search and modified_beam_search", + ) + + return parser + + +def decode_one_batch( + model: OnnxModel, token_table: k2.SymbolTable, batch: dict +) -> List[List[str]]: + """Decode one batch and return the result. + Currently it only greedy_search is supported. + + Args: + model: + The neural model. + token_table: + Mapping ids to tokens. + batch: + It is the return value from iterating + `lhotse.dataset.K2SpeechRecognitionDataset`. See its documentation + for the format of the `batch`. + + Returns: + Return the decoded results for each utterance. + """ + feature = batch["inputs"] + assert feature.ndim == 3 + # at entry, feature is (N, T, C) + + supervisions = batch["supervisions"] + feature_lens = supervisions["num_frames"].to(dtype=torch.int64) + + encoder_out, encoder_out_lens = model.run_encoder(x=feature, x_lens=feature_lens) + + hyps = greedy_search( + model=model, encoder_out=encoder_out, encoder_out_lens=encoder_out_lens + ) + + hyps = [[token_table[h] for h in hyp] for hyp in hyps] + return hyps + + +def decode_dataset( + dl: torch.utils.data.DataLoader, + model: nn.Module, + token_table: k2.SymbolTable, +) -> Tuple[List[Tuple[str, List[str], List[str]]], float]: + """Decode dataset. + + Args: + dl: + PyTorch's dataloader containing the dataset to decode. + model: + The neural model. + token_table: + Mapping ids to tokens. + + Returns: + - A list of tuples. Each tuple contains three elements: + - cut_id, + - reference transcript, + - predicted result. + - The total duration (in seconds) of the dataset. + """ + num_cuts = 0 + + try: + num_batches = len(dl) + except TypeError: + num_batches = "?" + + log_interval = 10 + total_duration = 0 + + results = [] + for batch_idx, batch in enumerate(dl): + texts = batch["supervisions"]["text"] + cut_ids = [cut.id for cut in batch["supervisions"]["cut"]] + total_duration += sum([cut.duration for cut in batch["supervisions"]["cut"]]) + + hyps = decode_one_batch(model=model, token_table=token_table, batch=batch) + + this_batch = [] + assert len(hyps) == len(texts) + for cut_id, hyp_words, ref_text in zip(cut_ids, hyps, texts): + ref_words = list(ref_text) + this_batch.append((cut_id, ref_words, hyp_words)) + + results.extend(this_batch) + + num_cuts += len(texts) + + if batch_idx % log_interval == 0: + batch_str = f"{batch_idx}/{num_batches}" + + logging.info(f"batch {batch_str}, cuts processed until now is {num_cuts}") + + return results, total_duration + + +def save_results( + res_dir: Path, + test_set_name: str, + results: List[Tuple[str, List[str], List[str]]], +): + recog_path = res_dir / f"recogs-{test_set_name}.txt" + results = sorted(results) + store_transcripts(filename=recog_path, texts=results) + logging.info(f"The transcripts are stored in {recog_path}") + + # The following prints out WERs, per-word error statistics and aligned + # ref/hyp pairs. + errs_filename = res_dir / f"errs-{test_set_name}.txt" + with open(errs_filename, "w") as f: + wer = write_error_stats(f, f"{test_set_name}", results, enable_log=True) + + logging.info("Wrote detailed error stats to {}".format(errs_filename)) + + errs_info = res_dir / f"wer-summary-{test_set_name}.txt" + with open(errs_info, "w") as f: + print("WER", file=f) + print(wer, file=f) + + s = "\nFor {}, WER is {}:\n".format(test_set_name, wer) + logging.info(s) + + +@torch.no_grad() +def main(): + parser = get_parser() + WenetSpeechAsrDataModule.add_arguments(parser) + args = parser.parse_args() + + assert ( + args.decoding_method == "greedy_search" + ), "Only supports greedy_search currently." + res_dir = Path(args.exp_dir) / f"onnx-{args.decoding_method}" + + setup_logger(f"{res_dir}/log-decode") + logging.info("Decoding started") + + device = torch.device("cpu") + logging.info(f"Device: {device}") + + token_table = k2.SymbolTable.from_file(args.tokens) + assert token_table[0] == "" + + logging.info(vars(args)) + + logging.info("About to create model") + model = OnnxModel( + encoder_model_filename=args.encoder_model_filename, + decoder_model_filename=args.decoder_model_filename, + joiner_model_filename=args.joiner_model_filename, + ) + + # we need cut ids to display recognition results. + args.return_cuts = True + + wenetspeech = WenetSpeechAsrDataModule(args) + + def remove_short_utt(c: Cut): + T = ((c.num_frames - 7) // 2 + 1) // 2 + if T <= 0: + logging.warning( + f"Exclude cut with ID {c.id} from decoding, num_frames : {c.num_frames}." + ) + return T > 0 + + dev_cuts = wenetspeech.valid_cuts() + dev_cuts = dev_cuts.filter(remove_short_utt) + dev_dl = wenetspeech.valid_dataloaders(dev_cuts) + + test_net_cuts = wenetspeech.test_net_cuts() + test_net_cuts = test_net_cuts.filter(remove_short_utt) + test_net_dl = wenetspeech.test_dataloaders(test_net_cuts) + + test_meeting_cuts = wenetspeech.test_meeting_cuts() + test_meeting_cuts = test_meeting_cuts.filter(remove_short_utt) + test_meeting_dl = wenetspeech.test_dataloaders(test_meeting_cuts) + + test_sets = ["DEV", "TEST_NET", "TEST_MEETING"] + test_dl = [dev_dl, test_net_dl, test_meeting_dl] + + for test_set, test_dl in zip(test_sets, test_dl): + start_time = time.time() + results, total_duration = decode_dataset( + dl=test_dl, model=model, token_table=token_table + ) + end_time = time.time() + elapsed_seconds = end_time - start_time + rtf = elapsed_seconds / total_duration + + logging.info(f"Elapsed time: {elapsed_seconds:.3f} s") + logging.info(f"Wave duration: {total_duration:.3f} s") + logging.info( + f"Real time factor (RTF): {elapsed_seconds:.3f}/{total_duration:.3f} = {rtf:.3f}" + ) + + save_results(res_dir=res_dir, test_set_name=test_set, results=results) + + logging.info("Done!") + + +if __name__ == "__main__": + main() diff --git a/egs/wenetspeech/ASR/zipformer/onnx_pretrained-streaming.py b/egs/wenetspeech/ASR/zipformer/onnx_pretrained-streaming.py new file mode 120000 index 0000000000..cfea104c27 --- /dev/null +++ b/egs/wenetspeech/ASR/zipformer/onnx_pretrained-streaming.py @@ -0,0 +1 @@ +../../../librispeech/ASR/zipformer/onnx_pretrained-streaming.py \ No newline at end of file diff --git a/egs/wenetspeech/ASR/zipformer/onnx_pretrained.py b/egs/wenetspeech/ASR/zipformer/onnx_pretrained.py new file mode 120000 index 0000000000..8f32f4ee7a --- /dev/null +++ b/egs/wenetspeech/ASR/zipformer/onnx_pretrained.py @@ -0,0 +1 @@ +../../../librispeech/ASR/zipformer/onnx_pretrained.py \ No newline at end of file diff --git a/egs/wenetspeech/ASR/zipformer/optim.py b/egs/wenetspeech/ASR/zipformer/optim.py new file mode 120000 index 0000000000..5eaa3cffd4 --- /dev/null +++ b/egs/wenetspeech/ASR/zipformer/optim.py @@ -0,0 +1 @@ +../../../librispeech/ASR/zipformer/optim.py \ No newline at end of file diff --git a/egs/wenetspeech/ASR/zipformer/pretrained.py b/egs/wenetspeech/ASR/zipformer/pretrained.py new file mode 120000 index 0000000000..0bd71dde4d --- /dev/null +++ b/egs/wenetspeech/ASR/zipformer/pretrained.py @@ -0,0 +1 @@ +../../../librispeech/ASR/zipformer/pretrained.py \ No newline at end of file diff --git a/egs/wenetspeech/ASR/zipformer/scaling.py b/egs/wenetspeech/ASR/zipformer/scaling.py new file mode 120000 index 0000000000..6f398f431d --- /dev/null +++ b/egs/wenetspeech/ASR/zipformer/scaling.py @@ -0,0 +1 @@ +../../../librispeech/ASR/zipformer/scaling.py \ No newline at end of file diff --git a/egs/wenetspeech/ASR/zipformer/scaling_converter.py b/egs/wenetspeech/ASR/zipformer/scaling_converter.py new file mode 120000 index 0000000000..b0ecee05e1 --- /dev/null +++ b/egs/wenetspeech/ASR/zipformer/scaling_converter.py @@ -0,0 +1 @@ +../../../librispeech/ASR/zipformer/scaling_converter.py \ No newline at end of file diff --git a/egs/wenetspeech/ASR/zipformer/streaming_beam_search.py b/egs/wenetspeech/ASR/zipformer/streaming_beam_search.py new file mode 120000 index 0000000000..b1ed545579 --- /dev/null +++ b/egs/wenetspeech/ASR/zipformer/streaming_beam_search.py @@ -0,0 +1 @@ +../../../librispeech/ASR/zipformer/streaming_beam_search.py \ No newline at end of file diff --git a/egs/wenetspeech/ASR/zipformer/streaming_decode.py b/egs/wenetspeech/ASR/zipformer/streaming_decode.py new file mode 100755 index 0000000000..94c5fae5f5 --- /dev/null +++ b/egs/wenetspeech/ASR/zipformer/streaming_decode.py @@ -0,0 +1,881 @@ +#!/usr/bin/env python3 +# Copyright 2022-2023 Xiaomi Corporation (Authors: Wei Kang, +# Fangjun Kuang, +# Zengwei Yao) +# +# See ../../../../LICENSE for clarification regarding multiple authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +""" +Usage: +./zipformer/streaming_decode.py \ + --epoch 28 \ + --avg 15 \ + --causal 1 \ + --chunk-size 16 \ + --left-context-frames 256 \ + --exp-dir ./zipformer/exp \ + --decoding-method greedy_search \ + --num-decode-streams 2000 +""" + +import argparse +import logging +import math +from pathlib import Path +from typing import Dict, List, Optional, Tuple + +import k2 +import numpy as np +import torch +from asr_datamodule import WenetSpeechAsrDataModule +from decode_stream import DecodeStream +from kaldifeat import Fbank, FbankOptions +from lhotse import CutSet +from streaming_beam_search import ( + fast_beam_search_one_best, + greedy_search, + modified_beam_search, +) +from torch import Tensor, nn +from torch.nn.utils.rnn import pad_sequence +from train import add_model_arguments, get_model, get_params + +from icefall.checkpoint import ( + average_checkpoints, + average_checkpoints_with_averaged_model, + find_checkpoints, + load_checkpoint, +) +from icefall.lexicon import Lexicon +from icefall.utils import ( + AttributeDict, + make_pad_mask, + setup_logger, + store_transcripts, + str2bool, + write_error_stats, +) + +LOG_EPS = math.log(1e-10) + + +def get_parser(): + parser = argparse.ArgumentParser( + formatter_class=argparse.ArgumentDefaultsHelpFormatter + ) + + parser.add_argument( + "--epoch", + type=int, + default=28, + help="""It specifies the checkpoint to use for decoding. + Note: Epoch counts from 1. + You can specify --avg to use more checkpoints for model averaging.""", + ) + + parser.add_argument( + "--iter", + type=int, + default=0, + help="""If positive, --epoch is ignored and it + will use the checkpoint exp_dir/checkpoint-iter.pt. + You can specify --avg to use more checkpoints for model averaging. + """, + ) + + parser.add_argument( + "--avg", + type=int, + default=15, + help="Number of checkpoints to average. Automatically select " + "consecutive checkpoints before the checkpoint specified by " + "'--epoch' and '--iter'", + ) + + parser.add_argument( + "--use-averaged-model", + type=str2bool, + default=True, + help="Whether to load averaged model. Currently it only supports " + "using --epoch. If True, it would decode with the averaged model " + "over the epoch range from `epoch-avg` (excluded) to `epoch`." + "Actually only the models with epoch number of `epoch-avg` and " + "`epoch` are loaded for averaging. ", + ) + + parser.add_argument( + "--exp-dir", + type=str, + default="zipformer/exp", + help="The experiment dir", + ) + + parser.add_argument( + "--lang-dir", + type=str, + default="data/lang_char", + help="Path to the lang dir(containing lexicon, tokens, etc.)", + ) + + parser.add_argument( + "--decoding-method", + type=str, + default="greedy_search", + help="""Supported decoding methods are: + greedy_search + modified_beam_search + fast_beam_search + """, + ) + + parser.add_argument( + "--num_active_paths", + type=int, + default=4, + help="""An interger indicating how many candidates we will keep for each + frame. Used only when --decoding-method is modified_beam_search.""", + ) + + parser.add_argument( + "--beam", + type=float, + default=4, + help="""A floating point value to calculate the cutoff score during beam + search (i.e., `cutoff = max-score - beam`), which is the same as the + `beam` in Kaldi. + Used only when --decoding-method is fast_beam_search""", + ) + + parser.add_argument( + "--max-contexts", + type=int, + default=4, + help="""Used only when --decoding-method is + fast_beam_search""", + ) + + parser.add_argument( + "--max-states", + type=int, + default=32, + help="""Used only when --decoding-method is + fast_beam_search""", + ) + + parser.add_argument( + "--context-size", + type=int, + default=2, + help="The context size in the decoder. 1 means bigram; 2 means tri-gram", + ) + + parser.add_argument( + "--blank-penalty", + type=float, + default=0.0, + help=""" + The penalty applied on blank symbol during decoding. + Note: It is a positive value that would be applied to logits like + this `logits[:, 0] -= blank_penalty` (suppose logits.shape is + [batch_size, vocab] and blank id is 0). + """, + ) + + parser.add_argument( + "--num-decode-streams", + type=int, + default=2000, + help="The number of streams that can be decoded parallel.", + ) + + add_model_arguments(parser) + + return parser + + +def get_init_states( + model: nn.Module, + batch_size: int = 1, + device: torch.device = torch.device("cpu"), +) -> List[torch.Tensor]: + """ + Returns a list of cached tensors of all encoder layers. For layer-i, states[i*6:(i+1)*6] + is (cached_key, cached_nonlin_attn, cached_val1, cached_val2, cached_conv1, cached_conv2). + states[-2] is the cached left padding for ConvNeXt module, + of shape (batch_size, num_channels, left_pad, num_freqs) + states[-1] is processed_lens of shape (batch,), which records the number + of processed frames (at 50hz frame rate, after encoder_embed) for each sample in batch. + """ + states = model.encoder.get_init_states(batch_size, device) + + embed_states = model.encoder_embed.get_init_states(batch_size, device) + states.append(embed_states) + + processed_lens = torch.zeros(batch_size, dtype=torch.int32, device=device) + states.append(processed_lens) + + return states + + +def stack_states(state_list: List[List[torch.Tensor]]) -> List[torch.Tensor]: + """Stack list of zipformer states that correspond to separate utterances + into a single emformer state, so that it can be used as an input for + zipformer when those utterances are formed into a batch. + + Args: + state_list: + Each element in state_list corresponding to the internal state + of the zipformer model for a single utterance. For element-n, + state_list[n] is a list of cached tensors of all encoder layers. For layer-i, + state_list[n][i*6:(i+1)*6] is (cached_key, cached_nonlin_attn, cached_val1, + cached_val2, cached_conv1, cached_conv2). + state_list[n][-2] is the cached left padding for ConvNeXt module, + of shape (batch_size, num_channels, left_pad, num_freqs) + state_list[n][-1] is processed_lens of shape (batch,), which records the number + of processed frames (at 50hz frame rate, after encoder_embed) for each sample in batch. + + Note: + It is the inverse of :func:`unstack_states`. + """ + batch_size = len(state_list) + assert (len(state_list[0]) - 2) % 6 == 0, len(state_list[0]) + tot_num_layers = (len(state_list[0]) - 2) // 6 + + batch_states = [] + for layer in range(tot_num_layers): + layer_offset = layer * 6 + # cached_key: (left_context_len, batch_size, key_dim) + cached_key = torch.cat( + [state_list[i][layer_offset] for i in range(batch_size)], dim=1 + ) + # cached_nonlin_attn: (num_heads, batch_size, left_context_len, head_dim) + cached_nonlin_attn = torch.cat( + [state_list[i][layer_offset + 1] for i in range(batch_size)], dim=1 + ) + # cached_val1: (left_context_len, batch_size, value_dim) + cached_val1 = torch.cat( + [state_list[i][layer_offset + 2] for i in range(batch_size)], dim=1 + ) + # cached_val2: (left_context_len, batch_size, value_dim) + cached_val2 = torch.cat( + [state_list[i][layer_offset + 3] for i in range(batch_size)], dim=1 + ) + # cached_conv1: (#batch, channels, left_pad) + cached_conv1 = torch.cat( + [state_list[i][layer_offset + 4] for i in range(batch_size)], dim=0 + ) + # cached_conv2: (#batch, channels, left_pad) + cached_conv2 = torch.cat( + [state_list[i][layer_offset + 5] for i in range(batch_size)], dim=0 + ) + batch_states += [ + cached_key, + cached_nonlin_attn, + cached_val1, + cached_val2, + cached_conv1, + cached_conv2, + ] + + cached_embed_left_pad = torch.cat( + [state_list[i][-2] for i in range(batch_size)], dim=0 + ) + batch_states.append(cached_embed_left_pad) + + processed_lens = torch.cat([state_list[i][-1] for i in range(batch_size)], dim=0) + batch_states.append(processed_lens) + + return batch_states + + +def unstack_states(batch_states: List[Tensor]) -> List[List[Tensor]]: + """Unstack the zipformer state corresponding to a batch of utterances + into a list of states, where the i-th entry is the state from the i-th + utterance in the batch. + + Note: + It is the inverse of :func:`stack_states`. + + Args: + batch_states: A list of cached tensors of all encoder layers. For layer-i, + states[i*6:(i+1)*6] is (cached_key, cached_nonlin_attn, cached_val1, cached_val2, + cached_conv1, cached_conv2). + state_list[-2] is the cached left padding for ConvNeXt module, + of shape (batch_size, num_channels, left_pad, num_freqs) + states[-1] is processed_lens of shape (batch,), which records the number + of processed frames (at 50hz frame rate, after encoder_embed) for each sample in batch. + + Returns: + state_list: A list of list. Each element in state_list corresponding to the internal state + of the zipformer model for a single utterance. + """ + assert (len(batch_states) - 2) % 6 == 0, len(batch_states) + tot_num_layers = (len(batch_states) - 2) // 6 + + processed_lens = batch_states[-1] + batch_size = processed_lens.shape[0] + + state_list = [[] for _ in range(batch_size)] + + for layer in range(tot_num_layers): + layer_offset = layer * 6 + # cached_key: (left_context_len, batch_size, key_dim) + cached_key_list = batch_states[layer_offset].chunk(chunks=batch_size, dim=1) + # cached_nonlin_attn: (num_heads, batch_size, left_context_len, head_dim) + cached_nonlin_attn_list = batch_states[layer_offset + 1].chunk( + chunks=batch_size, dim=1 + ) + # cached_val1: (left_context_len, batch_size, value_dim) + cached_val1_list = batch_states[layer_offset + 2].chunk( + chunks=batch_size, dim=1 + ) + # cached_val2: (left_context_len, batch_size, value_dim) + cached_val2_list = batch_states[layer_offset + 3].chunk( + chunks=batch_size, dim=1 + ) + # cached_conv1: (#batch, channels, left_pad) + cached_conv1_list = batch_states[layer_offset + 4].chunk( + chunks=batch_size, dim=0 + ) + # cached_conv2: (#batch, channels, left_pad) + cached_conv2_list = batch_states[layer_offset + 5].chunk( + chunks=batch_size, dim=0 + ) + for i in range(batch_size): + state_list[i] += [ + cached_key_list[i], + cached_nonlin_attn_list[i], + cached_val1_list[i], + cached_val2_list[i], + cached_conv1_list[i], + cached_conv2_list[i], + ] + + cached_embed_left_pad_list = batch_states[-2].chunk(chunks=batch_size, dim=0) + for i in range(batch_size): + state_list[i].append(cached_embed_left_pad_list[i]) + + processed_lens_list = batch_states[-1].chunk(chunks=batch_size, dim=0) + for i in range(batch_size): + state_list[i].append(processed_lens_list[i]) + + return state_list + + +def streaming_forward( + features: Tensor, + feature_lens: Tensor, + model: nn.Module, + states: List[Tensor], + chunk_size: int, + left_context_len: int, +) -> Tuple[Tensor, Tensor, List[Tensor]]: + """ + Returns encoder outputs, output lengths, and updated states. + """ + cached_embed_left_pad = states[-2] + (x, x_lens, new_cached_embed_left_pad,) = model.encoder_embed.streaming_forward( + x=features, + x_lens=feature_lens, + cached_left_pad=cached_embed_left_pad, + ) + assert x.size(1) == chunk_size, (x.size(1), chunk_size) + + src_key_padding_mask = make_pad_mask(x_lens) + + # processed_mask is used to mask out initial states + processed_mask = torch.arange(left_context_len, device=x.device).expand( + x.size(0), left_context_len + ) + processed_lens = states[-1] # (batch,) + # (batch, left_context_size) + processed_mask = (processed_lens.unsqueeze(1) <= processed_mask).flip(1) + # Update processed lengths + new_processed_lens = processed_lens + x_lens + + # (batch, left_context_size + chunk_size) + src_key_padding_mask = torch.cat([processed_mask, src_key_padding_mask], dim=1) + + x = x.permute(1, 0, 2) # (N, T, C) -> (T, N, C) + encoder_states = states[:-2] + ( + encoder_out, + encoder_out_lens, + new_encoder_states, + ) = model.encoder.streaming_forward( + x=x, + x_lens=x_lens, + states=encoder_states, + src_key_padding_mask=src_key_padding_mask, + ) + encoder_out = encoder_out.permute(1, 0, 2) # (T, N, C) ->(N, T, C) + + new_states = new_encoder_states + [ + new_cached_embed_left_pad, + new_processed_lens, + ] + return encoder_out, encoder_out_lens, new_states + + +def decode_one_chunk( + params: AttributeDict, + model: nn.Module, + decode_streams: List[DecodeStream], +) -> List[int]: + """Decode one chunk frames of features for each decode_streams and + return the indexes of finished streams in a List. + + Args: + params: + It's the return value of :func:`get_params`. + model: + The neural model. + decode_streams: + A List of DecodeStream, each belonging to a utterance. + Returns: + Return a List containing which DecodeStreams are finished. + """ + device = model.device + chunk_size = int(params.chunk_size) + left_context_len = int(params.left_context_frames) + + features = [] + feature_lens = [] + states = [] + processed_lens = [] # Used in fast-beam-search + + for stream in decode_streams: + feat, feat_len = stream.get_feature_frames(chunk_size * 2) + features.append(feat) + feature_lens.append(feat_len) + states.append(stream.states) + processed_lens.append(stream.done_frames) + + feature_lens = torch.tensor(feature_lens, device=device) + features = pad_sequence(features, batch_first=True, padding_value=LOG_EPS) + + # Make sure the length after encoder_embed is at least 1. + # The encoder_embed subsample features (T - 7) // 2 + # The ConvNeXt module needs (7 - 1) // 2 = 3 frames of right padding after subsampling + tail_length = chunk_size * 2 + 7 + 2 * 3 + if features.size(1) < tail_length: + pad_length = tail_length - features.size(1) + feature_lens += pad_length + features = torch.nn.functional.pad( + features, + (0, 0, 0, pad_length), + mode="constant", + value=LOG_EPS, + ) + + states = stack_states(states) + + encoder_out, encoder_out_lens, new_states = streaming_forward( + features=features, + feature_lens=feature_lens, + model=model, + states=states, + chunk_size=chunk_size, + left_context_len=left_context_len, + ) + + encoder_out = model.joiner.encoder_proj(encoder_out) + + if params.decoding_method == "greedy_search": + greedy_search( + model=model, + encoder_out=encoder_out, + streams=decode_streams, + blank_penalty=params.blank_penalty, + ) + elif params.decoding_method == "fast_beam_search": + processed_lens = torch.tensor(processed_lens, device=device) + processed_lens = processed_lens + encoder_out_lens + fast_beam_search_one_best( + model=model, + encoder_out=encoder_out, + processed_lens=processed_lens, + streams=decode_streams, + beam=params.beam, + max_states=params.max_states, + max_contexts=params.max_contexts, + blank_penalty=params.blank_penalty, + ) + elif params.decoding_method == "modified_beam_search": + modified_beam_search( + model=model, + streams=decode_streams, + encoder_out=encoder_out, + num_active_paths=params.num_active_paths, + blank_penalty=params.blank_penalty, + ) + else: + raise ValueError(f"Unsupported decoding method: {params.decoding_method}") + + states = unstack_states(new_states) + + finished_streams = [] + for i in range(len(decode_streams)): + decode_streams[i].states = states[i] + decode_streams[i].done_frames += encoder_out_lens[i] + if decode_streams[i].done: + finished_streams.append(i) + + return finished_streams + + +def decode_dataset( + cuts: CutSet, + params: AttributeDict, + model: nn.Module, + lexicon: Lexicon, + decoding_graph: Optional[k2.Fsa] = None, +) -> Dict[str, List[Tuple[List[str], List[str]]]]: + """Decode dataset. + + Args: + cuts: + Lhotse Cutset containing the dataset to decode. + params: + It is returned by :func:`get_params`. + model: + The neural model. + lexicon: + The Lexicon. + decoding_graph: + The decoding graph. Can be either a `k2.trivial_graph` or HLG, Used + only when --decoding_method is fast_beam_search. + Returns: + Return a dict, whose key may be "greedy_search" if greedy search + is used, or it may be "beam_7" if beam size of 7 is used. + Its value is a list of tuples. Each tuple contains two elements: + The first is the reference transcript, and the second is the + predicted result. + """ + device = model.device + + opts = FbankOptions() + opts.device = device + opts.frame_opts.dither = 0 + opts.frame_opts.snip_edges = False + opts.frame_opts.samp_freq = 16000 + opts.mel_opts.num_bins = 80 + + log_interval = 100 + + decode_results = [] + # Contain decode streams currently running. + decode_streams = [] + for num, cut in enumerate(cuts): + # each utterance has a DecodeStream. + initial_states = get_init_states(model=model, batch_size=1, device=device) + decode_stream = DecodeStream( + params=params, + cut_id=cut.id, + initial_states=initial_states, + decoding_graph=decoding_graph, + device=device, + ) + + audio: np.ndarray = cut.load_audio() + # audio.shape: (1, num_samples) + assert len(audio.shape) == 2 + assert audio.shape[0] == 1, "Should be single channel" + assert audio.dtype == np.float32, audio.dtype + + # The trained model is using normalized samples + if audio.max() > 1: + logging.warning( + f"The audio should be normalized to [-1, 1], audio.max : {audio.max()}." + f"Clipping to [-1, 1]." + ) + audio = np.clip(audio, -1, 1) + + samples = torch.from_numpy(audio).squeeze(0) + + fbank = Fbank(opts) + feature = fbank(samples.to(device)) + decode_stream.set_features(feature, tail_pad_len=30) + decode_stream.ground_truth = cut.supervisions[0].text + + decode_streams.append(decode_stream) + + while len(decode_streams) >= params.num_decode_streams: + finished_streams = decode_one_chunk( + params=params, model=model, decode_streams=decode_streams + ) + for i in sorted(finished_streams, reverse=True): + decode_results.append( + ( + decode_streams[i].id, + list(decode_streams[i].ground_truth.strip()), + [ + lexicon.token_table[idx] + for idx in decode_streams[i].decoding_result() + ], + ) + ) + del decode_streams[i] + + if num % log_interval == 0: + logging.info(f"Cuts processed until now is {num}.") + + # decode final chunks of last sequences + while len(decode_streams): + finished_streams = decode_one_chunk( + params=params, model=model, decode_streams=decode_streams + ) + for i in sorted(finished_streams, reverse=True): + decode_results.append( + ( + decode_streams[i].id, + decode_streams[i].ground_truth.split(), + [ + lexicon.token_table[idx] + for idx in decode_streams[i].decoding_result() + ], + ) + ) + del decode_streams[i] + + key = f"blank_penalty_{params.blank_penalty}" + if params.decoding_method == "greedy_search": + key = f"greedy_search_{key}" + elif params.decoding_method == "fast_beam_search": + key = ( + f"beam_{params.beam}_" + f"max_contexts_{params.max_contexts}_" + f"max_states_{params.max_states}_{key}" + ) + elif params.decoding_method == "modified_beam_search": + key = f"num_active_paths_{params.num_active_paths}_{key}" + else: + raise ValueError(f"Unsupported decoding method: {params.decoding_method}") + return {key: decode_results} + + +def save_results( + params: AttributeDict, + test_set_name: str, + results_dict: Dict[str, List[Tuple[List[str], List[str]]]], +): + test_set_wers = dict() + for key, results in results_dict.items(): + recog_path = ( + params.res_dir / f"recogs-{test_set_name}-{key}-{params.suffix}.txt" + ) + results = sorted(results) + store_transcripts(filename=recog_path, texts=results) + logging.info(f"The transcripts are stored in {recog_path}") + + # The following prints out WERs, per-word error statistics and aligned + # ref/hyp pairs. + errs_filename = ( + params.res_dir / f"errs-{test_set_name}-{key}-{params.suffix}.txt" + ) + with open(errs_filename, "w") as f: + wer = write_error_stats( + f, f"{test_set_name}-{key}", results, enable_log=True + ) + test_set_wers[key] = wer + + logging.info("Wrote detailed error stats to {}".format(errs_filename)) + + test_set_wers = sorted(test_set_wers.items(), key=lambda x: x[1]) + errs_info = ( + params.res_dir / f"wer-summary-{test_set_name}-{key}-{params.suffix}.txt" + ) + with open(errs_info, "w") as f: + print("settings\tWER", file=f) + for key, val in test_set_wers: + print("{}\t{}".format(key, val), file=f) + + s = "\nFor {}, WER of different settings are:\n".format(test_set_name) + note = "\tbest for {}".format(test_set_name) + for key, val in test_set_wers: + s += "{}\t{}{}\n".format(key, val, note) + note = "" + logging.info(s) + + +@torch.no_grad() +def main(): + parser = get_parser() + WenetSpeechAsrDataModule.add_arguments(parser) + args = parser.parse_args() + args.exp_dir = Path(args.exp_dir) + + params = get_params() + params.update(vars(args)) + + params.res_dir = params.exp_dir / "streaming" / params.decoding_method + + if params.iter > 0: + params.suffix = f"iter-{params.iter}-avg-{params.avg}" + else: + params.suffix = f"epoch-{params.epoch}-avg-{params.avg}" + + assert params.causal, params.causal + assert "," not in params.chunk_size, "chunk_size should be one value in decoding." + assert ( + "," not in params.left_context_frames + ), "left_context_frames should be one value in decoding." + params.suffix += f"-chunk-{params.chunk_size}" + params.suffix += f"-left-context-{params.left_context_frames}" + params.suffix += f"-blank-penalty-{params.blank_penalty}" + + # for fast_beam_search + if params.decoding_method == "fast_beam_search": + params.suffix += f"-beam-{params.beam}" + params.suffix += f"-max-contexts-{params.max_contexts}" + params.suffix += f"-max-states-{params.max_states}" + + if params.use_averaged_model: + params.suffix += "-use-averaged-model" + + setup_logger(f"{params.res_dir}/log-decode-{params.suffix}") + logging.info("Decoding started") + + device = torch.device("cpu") + if torch.cuda.is_available(): + device = torch.device("cuda", 0) + + logging.info(f"Device: {device}") + + lexicon = Lexicon(params.lang_dir) + params.blank_id = lexicon.token_table[""] + params.vocab_size = max(lexicon.tokens) + 1 + + logging.info(params) + + logging.info("About to create model") + model = get_model(params) + + if not params.use_averaged_model: + if params.iter > 0: + filenames = find_checkpoints(params.exp_dir, iteration=-params.iter)[ + : params.avg + ] + if len(filenames) == 0: + raise ValueError( + f"No checkpoints found for" + f" --iter {params.iter}, --avg {params.avg}" + ) + elif len(filenames) < params.avg: + raise ValueError( + f"Not enough checkpoints ({len(filenames)}) found for" + f" --iter {params.iter}, --avg {params.avg}" + ) + logging.info(f"averaging {filenames}") + model.to(device) + model.load_state_dict(average_checkpoints(filenames, device=device)) + elif params.avg == 1: + load_checkpoint(f"{params.exp_dir}/epoch-{params.epoch}.pt", model) + else: + start = params.epoch - params.avg + 1 + filenames = [] + for i in range(start, params.epoch + 1): + if start >= 0: + filenames.append(f"{params.exp_dir}/epoch-{i}.pt") + logging.info(f"averaging {filenames}") + model.to(device) + model.load_state_dict(average_checkpoints(filenames, device=device)) + else: + if params.iter > 0: + filenames = find_checkpoints(params.exp_dir, iteration=-params.iter)[ + : params.avg + 1 + ] + if len(filenames) == 0: + raise ValueError( + f"No checkpoints found for" + f" --iter {params.iter}, --avg {params.avg}" + ) + elif len(filenames) < params.avg + 1: + raise ValueError( + f"Not enough checkpoints ({len(filenames)}) found for" + f" --iter {params.iter}, --avg {params.avg}" + ) + filename_start = filenames[-1] + filename_end = filenames[0] + logging.info( + "Calculating the averaged model over iteration checkpoints" + f" from {filename_start} (excluded) to {filename_end}" + ) + model.to(device) + model.load_state_dict( + average_checkpoints_with_averaged_model( + filename_start=filename_start, + filename_end=filename_end, + device=device, + ) + ) + else: + assert params.avg > 0, params.avg + start = params.epoch - params.avg + assert start >= 1, start + filename_start = f"{params.exp_dir}/epoch-{start}.pt" + filename_end = f"{params.exp_dir}/epoch-{params.epoch}.pt" + logging.info( + f"Calculating the averaged model over epoch range from " + f"{start} (excluded) to {params.epoch}" + ) + model.to(device) + model.load_state_dict( + average_checkpoints_with_averaged_model( + filename_start=filename_start, + filename_end=filename_end, + device=device, + ) + ) + + model.to(device) + model.eval() + model.device = device + + decoding_graph = None + if params.decoding_method == "fast_beam_search": + decoding_graph = k2.trivial_graph(params.vocab_size - 1, device=device) + + num_param = sum([p.numel() for p in model.parameters()]) + logging.info(f"Number of model parameters: {num_param}") + + wenetspeech = WenetSpeechAsrDataModule(args) + + dev_cuts = wenetspeech.valid_cuts() + test_net_cuts = wenetspeech.test_net_cuts() + test_meeting_cuts = wenetspeech.test_meeting_cuts() + + test_sets = ["DEV", "TEST_NET", "TEST_MEETING"] + test_cuts = [dev_cuts, test_net_cuts, test_meeting_cuts] + + for test_set, test_cut in zip(test_sets, test_cuts): + results_dict = decode_dataset( + cuts=test_cut, + params=params, + model=model, + lexicon=lexicon, + decoding_graph=decoding_graph, + ) + save_results( + params=params, + test_set_name=test_set, + results_dict=results_dict, + ) + + logging.info("Done!") + + +if __name__ == "__main__": + main() diff --git a/egs/wenetspeech/ASR/zipformer/subsampling.py b/egs/wenetspeech/ASR/zipformer/subsampling.py new file mode 120000 index 0000000000..01ae9002c6 --- /dev/null +++ b/egs/wenetspeech/ASR/zipformer/subsampling.py @@ -0,0 +1 @@ +../../../librispeech/ASR/zipformer/subsampling.py \ No newline at end of file diff --git a/egs/wenetspeech/ASR/zipformer/train.py b/egs/wenetspeech/ASR/zipformer/train.py new file mode 100755 index 0000000000..83dbfa22fb --- /dev/null +++ b/egs/wenetspeech/ASR/zipformer/train.py @@ -0,0 +1,1350 @@ +#!/usr/bin/env python3 +# Copyright 2021-2023 Xiaomi Corp. (authors: Fangjun Kuang, +# Wei Kang, +# Mingshuang Luo, +# Zengwei Yao, +# Daniel Povey) +# +# See ../../../../LICENSE for clarification regarding multiple authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" +Usage: + +export CUDA_VISIBLE_DEVICES="0,1,2,3,4,5,6,7" + +./zipformer/train.py \ + --world-size 8 \ + --num-epochs 12 \ + --start-epoch 1 \ + --exp-dir zipformer/exp \ + --training-subset L + --lr-epochs 1.5 \ + --max-duration 350 + +# For mix precision training: + +./zipformer/train.py \ + --world-size 8 \ + --num-epochs 12 \ + --start-epoch 1 \ + --use-fp16 1 \ + --exp-dir zipformer/exp \ + --training-subset L \ + --lr-epochs 1.5 \ + --max-duration 750 + +""" + + +import argparse +import copy +import logging +import warnings +from pathlib import Path +from shutil import copyfile +from typing import Any, Dict, Optional, Tuple, Union + +import k2 +import optim +import torch +import torch.multiprocessing as mp +import torch.nn as nn +from asr_datamodule import WenetSpeechAsrDataModule +from decoder import Decoder +from joiner import Joiner +from lhotse.cut import Cut +from lhotse.dataset.sampling.base import CutSampler +from lhotse.utils import fix_random_seed +from model import AsrModel +from optim import Eden, ScaledAdam +from scaling import ScheduledFloat +from subsampling import Conv2dSubsampling +from torch import Tensor +from torch.cuda.amp import GradScaler +from torch.nn.parallel import DistributedDataParallel as DDP +from torch.utils.tensorboard import SummaryWriter +from zipformer import Zipformer2 + +from icefall import diagnostics +from icefall.char_graph_compiler import CharCtcTrainingGraphCompiler +from icefall.checkpoint import load_checkpoint, remove_checkpoints +from icefall.checkpoint import save_checkpoint as save_checkpoint_impl +from icefall.checkpoint import ( + save_checkpoint_with_global_batch_idx, + update_averaged_model, +) +from icefall.dist import cleanup_dist, setup_dist +from icefall.env import get_env_info +from icefall.hooks import register_inf_check_hooks +from icefall.lexicon import Lexicon +from icefall.utils import ( + AttributeDict, + MetricsTracker, + get_parameter_groups_with_lrs, + setup_logger, + str2bool, +) + +LRSchedulerType = Union[torch.optim.lr_scheduler._LRScheduler, optim.LRScheduler] + + +def get_adjusted_batch_count(params: AttributeDict) -> float: + # returns the number of batches we would have used so far if we had used the reference + # duration. This is for purposes of set_batch_count(). + return ( + params.batch_idx_train + * (params.max_duration * params.world_size) + / params.ref_duration + ) + + +def set_batch_count(model: Union[nn.Module, DDP], batch_count: float) -> None: + if isinstance(model, DDP): + # get underlying nn.Module + model = model.module + for name, module in model.named_modules(): + if hasattr(module, "batch_count"): + module.batch_count = batch_count + if hasattr(module, "name"): + module.name = name + + +def add_model_arguments(parser: argparse.ArgumentParser): + parser.add_argument( + "--num-encoder-layers", + type=str, + default="2,2,3,4,3,2", + help="Number of zipformer encoder layers per stack, comma separated.", + ) + + parser.add_argument( + "--downsampling-factor", + type=str, + default="1,2,4,8,4,2", + help="Downsampling factor for each stack of encoder layers.", + ) + + parser.add_argument( + "--feedforward-dim", + type=str, + default="512,768,1024,1536,1024,768", + help="""Feedforward dimension of the zipformer encoder layers, per stack, comma separated.""", + ) + + parser.add_argument( + "--num-heads", + type=str, + default="4,4,4,8,4,4", + help="""Number of attention heads in the zipformer encoder layers: a single int or comma-separated list.""", + ) + + parser.add_argument( + "--encoder-dim", + type=str, + default="192,256,384,512,384,256", + help="""Embedding dimension in encoder stacks: a single int or comma-separated list.""", + ) + + parser.add_argument( + "--query-head-dim", + type=str, + default="32", + help="""Query/key dimension per head in encoder stacks: a single int or comma-separated list.""", + ) + + parser.add_argument( + "--value-head-dim", + type=str, + default="12", + help="""Value dimension per head in encoder stacks: a single int or comma-separated list.""", + ) + + parser.add_argument( + "--pos-head-dim", + type=str, + default="4", + help="""Positional-encoding dimension per head in encoder stacks: a single int or comma-separated list.""", + ) + + parser.add_argument( + "--pos-dim", + type=int, + default="48", + help="Positional-encoding embedding dimension", + ) + + parser.add_argument( + "--encoder-unmasked-dim", + type=str, + default="192,192,256,256,256,192", + help="""Unmasked dimensions in the encoders, relates to augmentation during training. A single int or comma-separated list. Must be <= each corresponding encoder_dim.""", + ) + + parser.add_argument( + "--cnn-module-kernel", + type=str, + default="31,31,15,15,15,31", + help="""Sizes of convolutional kernels in convolution modules in each encoder stack: a single int or comma-separated list.""", + ) + + parser.add_argument( + "--decoder-dim", + type=int, + default=512, + help="Embedding dimension in the decoder model.", + ) + + parser.add_argument( + "--joiner-dim", + type=int, + default=512, + help="""Dimension used in the joiner model. + Outputs from the encoder and decoder model are projected + to this dimension before adding. + """, + ) + + parser.add_argument( + "--causal", + type=str2bool, + default=False, + help="If True, use causal version of model.", + ) + + parser.add_argument( + "--chunk-size", + type=str, + default="16,32,64,-1", + help="""Chunk sizes (at 50Hz frame rate) will be chosen randomly from this list during training. Must be just -1 if --causal=False""", + ) + + parser.add_argument( + "--left-context-frames", + type=str, + default="64,128,256,-1", + help="""Maximum left-contexts for causal training, measured in frames which will + be converted to a number of chunks. If splitting into chunks, + chunk left-context frames will be chosen randomly from this list; else not relevant.""", + ) + + +def get_parser(): + parser = argparse.ArgumentParser( + formatter_class=argparse.ArgumentDefaultsHelpFormatter + ) + + parser.add_argument( + "--world-size", + type=int, + default=1, + help="Number of GPUs for DDP training.", + ) + + parser.add_argument( + "--master-port", + type=int, + default=12354, + help="Master port to use for DDP training.", + ) + + parser.add_argument( + "--tensorboard", + type=str2bool, + default=True, + help="Should various information be logged in tensorboard.", + ) + + parser.add_argument( + "--num-epochs", + type=int, + default=30, + help="Number of epochs to train.", + ) + + parser.add_argument( + "--start-epoch", + type=int, + default=1, + help="""Resume training from this epoch. It should be positive. + If larger than 1, it will load checkpoint from + exp-dir/epoch-{start_epoch-1}.pt + """, + ) + + parser.add_argument( + "--start-batch", + type=int, + default=0, + help="""If positive, --start-epoch is ignored and + it loads the checkpoint from exp-dir/checkpoint-{start_batch}.pt + """, + ) + + parser.add_argument( + "--exp-dir", + type=str, + default="zipformer/exp", + help="""The experiment dir. + It specifies the directory where all training related + files, e.g., checkpoints, log, etc, are saved + """, + ) + + parser.add_argument( + "--lang-dir", + type=str, + default="data/lang_char", + help="""The lang dir + It contains language related input files such as + "lexicon.txt" + """, + ) + + parser.add_argument( + "--base-lr", type=float, default=0.045, help="The base learning rate." + ) + + parser.add_argument( + "--lr-batches", + type=float, + default=7500, + help="""Number of steps that affects how rapidly the learning rate + decreases. We suggest not to change this.""", + ) + + parser.add_argument( + "--lr-epochs", + type=float, + default=3.5, + help="""Number of epochs that affects how rapidly the learning rate decreases. + """, + ) + + parser.add_argument( + "--ref-duration", + type=float, + default=600, + help="""Reference batch duration for purposes of adjusting batch counts for setting various schedules inside the model""", + ) + + parser.add_argument( + "--context-size", + type=int, + default=2, + help="""The context size in the decoder. 1 means bigram; 2 means tri-gram""", + ) + + parser.add_argument( + "--prune-range", + type=int, + default=5, + help="""The prune range for rnnt loss, it means how many symbols(context) + we are using to compute the loss""", + ) + + parser.add_argument( + "--lm-scale", + type=float, + default=0.25, + help="""The scale to smooth the loss with lm + (output of prediction network) part.""", + ) + + parser.add_argument( + "--am-scale", + type=float, + default=0.0, + help="""The scale to smooth the loss with am (output of encoder network) part.""", + ) + + parser.add_argument( + "--simple-loss-scale", + type=float, + default=0.5, + help="""To get pruning ranges, we will calculate a simple version + loss(joiner is just addition), this simple loss also uses for + training (as a regularization item). We will scale the simple loss + with this parameter before adding to the final loss.""", + ) + + parser.add_argument( + "--seed", + type=int, + default=42, + help="The seed for random generators intended for reproducibility", + ) + + parser.add_argument( + "--print-diagnostics", + type=str2bool, + default=False, + help="Accumulate stats on activations, print them and exit.", + ) + + parser.add_argument( + "--inf-check", + type=str2bool, + default=False, + help="Add hooks to check for infinite module outputs and gradients.", + ) + + parser.add_argument( + "--save-every-n", + type=int, + default=4000, + help="""Save checkpoint after processing this number of batches" + periodically. We save checkpoint to exp-dir/ whenever + params.batch_idx_train % save_every_n == 0. The checkpoint filename + has the form: f'exp-dir/checkpoint-{params.batch_idx_train}.pt' + Note: It also saves checkpoint to `exp-dir/epoch-xxx.pt` at the + end of each epoch where `xxx` is the epoch number counting from 0. + """, + ) + + parser.add_argument( + "--keep-last-k", + type=int, + default=30, + help="""Only keep this number of checkpoints on disk. + For instance, if it is 3, there are only 3 checkpoints + in the exp-dir with filenames `checkpoint-xxx.pt`. + It does not affect checkpoints with name `epoch-xxx.pt`. + """, + ) + + parser.add_argument( + "--average-period", + type=int, + default=200, + help="""Update the averaged model, namely `model_avg`, after processing + this number of batches. `model_avg` is a separate version of model, + in which each floating-point parameter is the average of all the + parameters from the start of training. Each time we take the average, + we do: `model_avg = model * (average_period / batch_idx_train) + + model_avg * ((batch_idx_train - average_period) / batch_idx_train)`. + """, + ) + + parser.add_argument( + "--use-fp16", + type=str2bool, + default=False, + help="Whether to use half precision training.", + ) + + add_model_arguments(parser) + + return parser + + +def get_params() -> AttributeDict: + """Return a dict containing training parameters. + + All training related parameters that are not passed from the commandline + are saved in the variable `params`. + + Commandline options are merged into `params` after they are parsed, so + you can also access them via `params`. + + Explanation of options saved in `params`: + + - best_train_loss: Best training loss so far. It is used to select + the model that has the lowest training loss. It is + updated during the training. + + - best_valid_loss: Best validation loss so far. It is used to select + the model that has the lowest validation loss. It is + updated during the training. + + - best_train_epoch: It is the epoch that has the best training loss. + + - best_valid_epoch: It is the epoch that has the best validation loss. + + - batch_idx_train: Used to writing statistics to tensorboard. It + contains number of batches trained so far across + epochs. + + - log_interval: Print training loss if batch_idx % log_interval` is 0 + + - reset_interval: Reset statistics if batch_idx % reset_interval is 0 + + - valid_interval: Run validation if batch_idx % valid_interval is 0 + + - feature_dim: The model input dim. It has to match the one used + in computing features. + + - subsampling_factor: The subsampling factor for the model. + + - encoder_dim: Hidden dim for multi-head attention model. + + - num_decoder_layers: Number of decoder layer of transformer decoder. + + - warm_step: The warmup period that dictates the decay of the + scale on "simple" (un-pruned) loss. + """ + params = AttributeDict( + { + "best_train_loss": float("inf"), + "best_valid_loss": float("inf"), + "best_train_epoch": -1, + "best_valid_epoch": -1, + "batch_idx_train": 0, + "log_interval": 50, + "reset_interval": 200, + "valid_interval": 3000, + # parameters for zipformer + "feature_dim": 80, + "subsampling_factor": 4, # not passed in, this is fixed. + "warm_step": 2000, + "env_info": get_env_info(), + } + ) + + return params + + +def _to_int_tuple(s: str): + return tuple(map(int, s.split(","))) + + +def get_encoder_embed(params: AttributeDict) -> nn.Module: + # encoder_embed converts the input of shape (N, T, num_features) + # to the shape (N, (T - 7) // 2, encoder_dims). + # That is, it does two things simultaneously: + # (1) subsampling: T -> (T - 7) // 2 + # (2) embedding: num_features -> encoder_dims + # In the normal configuration, we will downsample once more at the end + # by a factor of 2, and most of the encoder stacks will run at a lower + # sampling rate. + encoder_embed = Conv2dSubsampling( + in_channels=params.feature_dim, + out_channels=_to_int_tuple(params.encoder_dim)[0], + dropout=ScheduledFloat((0.0, 0.3), (20000.0, 0.1)), + ) + return encoder_embed + + +def get_encoder_model(params: AttributeDict) -> nn.Module: + encoder = Zipformer2( + output_downsampling_factor=2, + downsampling_factor=_to_int_tuple(params.downsampling_factor), + num_encoder_layers=_to_int_tuple(params.num_encoder_layers), + encoder_dim=_to_int_tuple(params.encoder_dim), + encoder_unmasked_dim=_to_int_tuple(params.encoder_unmasked_dim), + query_head_dim=_to_int_tuple(params.query_head_dim), + pos_head_dim=_to_int_tuple(params.pos_head_dim), + value_head_dim=_to_int_tuple(params.value_head_dim), + pos_dim=params.pos_dim, + num_heads=_to_int_tuple(params.num_heads), + feedforward_dim=_to_int_tuple(params.feedforward_dim), + cnn_module_kernel=_to_int_tuple(params.cnn_module_kernel), + dropout=ScheduledFloat((0.0, 0.3), (20000.0, 0.1)), + warmup_batches=4000.0, + causal=params.causal, + chunk_size=_to_int_tuple(params.chunk_size), + left_context_frames=_to_int_tuple(params.left_context_frames), + ) + return encoder + + +def get_decoder_model(params: AttributeDict) -> nn.Module: + decoder = Decoder( + vocab_size=params.vocab_size, + decoder_dim=params.decoder_dim, + blank_id=params.blank_id, + context_size=params.context_size, + ) + return decoder + + +def get_joiner_model(params: AttributeDict) -> nn.Module: + joiner = Joiner( + encoder_dim=max(_to_int_tuple(params.encoder_dim)), + decoder_dim=params.decoder_dim, + joiner_dim=params.joiner_dim, + vocab_size=params.vocab_size, + ) + return joiner + + +def get_model(params: AttributeDict) -> nn.Module: + encoder_embed = get_encoder_embed(params) + encoder = get_encoder_model(params) + decoder = get_decoder_model(params) + joiner = get_joiner_model(params) + + model = AsrModel( + encoder_embed=encoder_embed, + encoder=encoder, + decoder=decoder, + joiner=joiner, + encoder_dim=int(max(params.encoder_dim.split(","))), + decoder_dim=params.decoder_dim, + vocab_size=params.vocab_size, + ) + return model + + +def load_checkpoint_if_available( + params: AttributeDict, + model: nn.Module, + model_avg: nn.Module = None, + optimizer: Optional[torch.optim.Optimizer] = None, + scheduler: Optional[LRSchedulerType] = None, +) -> Optional[Dict[str, Any]]: + """Load checkpoint from file. + + If params.start_batch is positive, it will load the checkpoint from + `params.exp_dir/checkpoint-{params.start_batch}.pt`. Otherwise, if + params.start_epoch is larger than 1, it will load the checkpoint from + `params.start_epoch - 1`. + + Apart from loading state dict for `model` and `optimizer` it also updates + `best_train_epoch`, `best_train_loss`, `best_valid_epoch`, + and `best_valid_loss` in `params`. + + Args: + params: + The return value of :func:`get_params`. + model: + The training model. + model_avg: + The stored model averaged from the start of training. + optimizer: + The optimizer that we are using. + scheduler: + The scheduler that we are using. + Returns: + Return a dict containing previously saved training info. + """ + if params.start_batch > 0: + filename = params.exp_dir / f"checkpoint-{params.start_batch}.pt" + elif params.start_epoch > 1: + filename = params.exp_dir / f"epoch-{params.start_epoch-1}.pt" + else: + return None + + assert filename.is_file(), f"{filename} does not exist!" + + saved_params = load_checkpoint( + filename, + model=model, + model_avg=model_avg, + optimizer=optimizer, + scheduler=scheduler, + ) + + keys = [ + "best_train_epoch", + "best_valid_epoch", + "batch_idx_train", + "best_train_loss", + "best_valid_loss", + ] + for k in keys: + params[k] = saved_params[k] + + if params.start_batch > 0: + if "cur_epoch" in saved_params: + params["start_epoch"] = saved_params["cur_epoch"] + + if "cur_batch_idx" in saved_params: + params["cur_batch_idx"] = saved_params["cur_batch_idx"] + + return saved_params + + +def save_checkpoint( + params: AttributeDict, + model: Union[nn.Module, DDP], + model_avg: Optional[nn.Module] = None, + optimizer: Optional[torch.optim.Optimizer] = None, + scheduler: Optional[LRSchedulerType] = None, + sampler: Optional[CutSampler] = None, + scaler: Optional[GradScaler] = None, + rank: int = 0, +) -> None: + """Save model, optimizer, scheduler and training stats to file. + + Args: + params: + It is returned by :func:`get_params`. + model: + The training model. + model_avg: + The stored model averaged from the start of training. + optimizer: + The optimizer used in the training. + sampler: + The sampler for the training dataset. + scaler: + The scaler used for mix precision training. + """ + if rank != 0: + return + filename = params.exp_dir / f"epoch-{params.cur_epoch}.pt" + save_checkpoint_impl( + filename=filename, + model=model, + model_avg=model_avg, + params=params, + optimizer=optimizer, + scheduler=scheduler, + sampler=sampler, + scaler=scaler, + rank=rank, + ) + + if params.best_train_epoch == params.cur_epoch: + best_train_filename = params.exp_dir / "best-train-loss.pt" + copyfile(src=filename, dst=best_train_filename) + + if params.best_valid_epoch == params.cur_epoch: + best_valid_filename = params.exp_dir / "best-valid-loss.pt" + copyfile(src=filename, dst=best_valid_filename) + + +def compute_loss( + params: AttributeDict, + model: Union[nn.Module, DDP], + graph_compiler: CharCtcTrainingGraphCompiler, + batch: dict, + is_training: bool, +) -> Tuple[Tensor, MetricsTracker]: + """ + Compute CTC loss given the model and its inputs. + + Args: + params: + Parameters for training. See :func:`get_params`. + model: + The model for training. It is an instance of Zipformer in our case. + batch: + A batch of data. See `lhotse.dataset.K2SpeechRecognitionDataset()` + for the content in it. + is_training: + True for training. False for validation. When it is True, this + function enables autograd during computation; when it is False, it + disables autograd. + warmup: a floating point value which increases throughout training; + values >= 1.0 are fully warmed up and have all modules present. + """ + device = model.device if isinstance(model, DDP) else next(model.parameters()).device + feature = batch["inputs"] + # at entry, feature is (N, T, C) + assert feature.ndim == 3 + feature = feature.to(device) + + supervisions = batch["supervisions"] + feature_lens = supervisions["num_frames"].to(device) + + batch_idx_train = params.batch_idx_train + warm_step = params.warm_step + + texts = batch["supervisions"]["text"] + y = graph_compiler.texts_to_ids(texts) + y = k2.RaggedTensor(y).to(device) + + with torch.set_grad_enabled(is_training): + simple_loss, pruned_loss, _ = model( + x=feature, + x_lens=feature_lens, + y=y, + prune_range=params.prune_range, + am_scale=params.am_scale, + lm_scale=params.lm_scale, + ) + + s = params.simple_loss_scale + # take down the scale on the simple loss from 1.0 at the start + # to params.simple_loss scale by warm_step. + simple_loss_scale = ( + s + if batch_idx_train >= warm_step + else 1.0 - (batch_idx_train / warm_step) * (1.0 - s) + ) + pruned_loss_scale = ( + 1.0 + if batch_idx_train >= warm_step + else 0.1 + 0.9 * (batch_idx_train / warm_step) + ) + + loss = simple_loss_scale * simple_loss + pruned_loss_scale * pruned_loss + + assert loss.requires_grad == is_training + + info = MetricsTracker() + with warnings.catch_warnings(): + warnings.simplefilter("ignore") + info["frames"] = (feature_lens // params.subsampling_factor).sum().item() + + # Note: We use reduction=sum while computing the loss. + info["loss"] = loss.detach().cpu().item() + info["simple_loss"] = simple_loss.detach().cpu().item() + info["pruned_loss"] = pruned_loss.detach().cpu().item() + + return loss, info + + +def compute_validation_loss( + params: AttributeDict, + model: Union[nn.Module, DDP], + graph_compiler: CharCtcTrainingGraphCompiler, + valid_dl: torch.utils.data.DataLoader, + world_size: int = 1, +) -> MetricsTracker: + """Run the validation process.""" + model.eval() + + tot_loss = MetricsTracker() + + for batch_idx, batch in enumerate(valid_dl): + loss, loss_info = compute_loss( + params=params, + model=model, + graph_compiler=graph_compiler, + batch=batch, + is_training=False, + ) + assert loss.requires_grad is False + tot_loss = tot_loss + loss_info + + if world_size > 1: + tot_loss.reduce(loss.device) + + loss_value = tot_loss["loss"] / tot_loss["frames"] + if loss_value < params.best_valid_loss: + params.best_valid_epoch = params.cur_epoch + params.best_valid_loss = loss_value + + return tot_loss + + +def train_one_epoch( + params: AttributeDict, + model: Union[nn.Module, DDP], + optimizer: torch.optim.Optimizer, + scheduler: LRSchedulerType, + graph_compiler: CharCtcTrainingGraphCompiler, + train_dl: torch.utils.data.DataLoader, + valid_dl: torch.utils.data.DataLoader, + scaler: GradScaler, + model_avg: Optional[nn.Module] = None, + tb_writer: Optional[SummaryWriter] = None, + world_size: int = 1, + rank: int = 0, +) -> None: + """Train the model for one epoch. + + The training loss from the mean of all frames is saved in + `params.train_loss`. It runs the validation process every + `params.valid_interval` batches. + + Args: + params: + It is returned by :func:`get_params`. + model: + The model for training. + optimizer: + The optimizer we are using. + scheduler: + The learning rate scheduler, we call step() every step. + train_dl: + Dataloader for the training dataset. + valid_dl: + Dataloader for the validation dataset. + scaler: + The scaler used for mix precision training. + model_avg: + The stored model averaged from the start of training. + tb_writer: + Writer to write log messages to tensorboard. + world_size: + Number of nodes in DDP training. If it is 1, DDP is disabled. + rank: + The rank of the node in DDP training. If no DDP is used, it should + be set to 0. + """ + model.train() + + tot_loss = MetricsTracker() + + cur_batch_idx = params.get("cur_batch_idx", 0) + + saved_bad_model = False + + def save_bad_model(suffix: str = ""): + save_checkpoint_impl( + filename=params.exp_dir / f"bad-model{suffix}-{rank}.pt", + model=model, + model_avg=model_avg, + params=params, + optimizer=optimizer, + scheduler=scheduler, + sampler=train_dl.sampler, + scaler=scaler, + rank=0, + ) + + for batch_idx, batch in enumerate(train_dl): + if batch_idx % 10 == 0: + set_batch_count(model, get_adjusted_batch_count(params)) + if batch_idx < cur_batch_idx: + continue + cur_batch_idx = batch_idx + + params.batch_idx_train += 1 + batch_size = len(batch["supervisions"]["text"]) + + try: + with torch.cuda.amp.autocast(enabled=params.use_fp16): + loss, loss_info = compute_loss( + params=params, + model=model, + graph_compiler=graph_compiler, + batch=batch, + is_training=True, + ) + # summary stats + tot_loss = (tot_loss * (1 - 1 / params.reset_interval)) + loss_info + + # NOTE: We use reduction==sum and loss is computed over utterances + # in the batch and there is no normalization to it so far. + scaler.scale(loss).backward() + scheduler.step_batch(params.batch_idx_train) + + scaler.step(optimizer) + scaler.update() + optimizer.zero_grad() + except: # noqa + save_bad_model() + display_and_save_batch(batch, params=params, graph_compiler=graph_compiler) + raise + + if params.print_diagnostics and batch_idx == 5: + return + + if ( + rank == 0 + and params.batch_idx_train > 0 + and params.batch_idx_train % params.average_period == 0 + ): + update_averaged_model( + params=params, + model_cur=model, + model_avg=model_avg, + ) + + if ( + params.batch_idx_train > 0 + and params.batch_idx_train % params.save_every_n == 0 + ): + params.cur_batch_idx = batch_idx + save_checkpoint_with_global_batch_idx( + out_dir=params.exp_dir, + global_batch_idx=params.batch_idx_train, + model=model, + model_avg=model_avg, + params=params, + optimizer=optimizer, + scheduler=scheduler, + sampler=train_dl.sampler, + scaler=scaler, + rank=rank, + ) + del params.cur_batch_idx + remove_checkpoints( + out_dir=params.exp_dir, + topk=params.keep_last_k, + rank=rank, + ) + + if batch_idx % 100 == 0 and params.use_fp16: + # If the grad scale was less than 1, try increasing it. The _growth_interval + # of the grad scaler is configurable, but we can't configure it to have different + # behavior depending on the current grad scale. + cur_grad_scale = scaler._scale.item() + + if cur_grad_scale < 8.0 or (cur_grad_scale < 32.0 and batch_idx % 400 == 0): + scaler.update(cur_grad_scale * 2.0) + if cur_grad_scale < 0.01: + if not saved_bad_model: + save_bad_model(suffix="-first-warning") + saved_bad_model = True + logging.warning(f"Grad scale is small: {cur_grad_scale}") + if cur_grad_scale < 1.0e-05: + save_bad_model() + raise RuntimeError( + f"grad_scale is too small, exiting: {cur_grad_scale}" + ) + + if batch_idx % params.log_interval == 0: + cur_lr = max(scheduler.get_last_lr()) + cur_grad_scale = scaler._scale.item() if params.use_fp16 else 1.0 + + logging.info( + f"Epoch {params.cur_epoch}, " + f"batch {batch_idx}, loss[{loss_info}], " + f"tot_loss[{tot_loss}], batch size: {batch_size}, " + f"lr: {cur_lr:.2e}, " + + (f"grad_scale: {scaler._scale.item()}" if params.use_fp16 else "") + ) + + if tb_writer is not None: + tb_writer.add_scalar( + "train/learning_rate", cur_lr, params.batch_idx_train + ) + + loss_info.write_summary( + tb_writer, "train/current_", params.batch_idx_train + ) + tot_loss.write_summary(tb_writer, "train/tot_", params.batch_idx_train) + if params.use_fp16: + tb_writer.add_scalar( + "train/grad_scale", cur_grad_scale, params.batch_idx_train + ) + + if batch_idx % params.valid_interval == 0 and not params.print_diagnostics: + logging.info("Computing validation loss") + valid_info = compute_validation_loss( + params=params, + model=model, + graph_compiler=graph_compiler, + valid_dl=valid_dl, + world_size=world_size, + ) + model.train() + logging.info(f"Epoch {params.cur_epoch}, validation: {valid_info}") + logging.info( + f"Maximum memory allocated so far is {torch.cuda.max_memory_allocated()//1000000}MB" + ) + if tb_writer is not None: + valid_info.write_summary( + tb_writer, "train/valid_", params.batch_idx_train + ) + + loss_value = tot_loss["loss"] / tot_loss["frames"] + params.train_loss = loss_value + if params.train_loss < params.best_train_loss: + params.best_train_epoch = params.cur_epoch + params.best_train_loss = params.train_loss + + +def run(rank, world_size, args): + """ + Args: + rank: + It is a value between 0 and `world_size-1`, which is + passed automatically by `mp.spawn()` in :func:`main`. + The node with rank 0 is responsible for saving checkpoint. + world_size: + Number of GPUs for DDP training. + args: + The return value of get_parser().parse_args() + """ + params = get_params() + params.update(vars(args)) + + fix_random_seed(params.seed) + if world_size > 1: + setup_dist(rank, world_size, params.master_port) + + setup_logger(f"{params.exp_dir}/log/log-train") + logging.info("Training started") + + if args.tensorboard and rank == 0: + tb_writer = SummaryWriter(log_dir=f"{params.exp_dir}/tensorboard") + else: + tb_writer = None + + device = torch.device("cpu") + if torch.cuda.is_available(): + device = torch.device("cuda", rank) + logging.info(f"Device: {device}") + + lexicon = Lexicon(params.lang_dir) + graph_compiler = CharCtcTrainingGraphCompiler( + lexicon=lexicon, + device=device, + ) + + params.blank_id = lexicon.token_table[""] + params.vocab_size = max(lexicon.tokens) + 1 + + logging.info(params) + + logging.info("About to create model") + model = get_model(params) + + num_param = sum([p.numel() for p in model.parameters()]) + logging.info(f"Number of model parameters: {num_param}") + + assert params.save_every_n >= params.average_period + model_avg: Optional[nn.Module] = None + if rank == 0: + # model_avg is only used with rank 0 + model_avg = copy.deepcopy(model).to(torch.float64) + + assert params.start_epoch > 0, params.start_epoch + checkpoints = load_checkpoint_if_available( + params=params, model=model, model_avg=model_avg + ) + + model.to(device) + if world_size > 1: + logging.info("Using DDP") + model = DDP(model, device_ids=[rank], find_unused_parameters=True) + + optimizer = ScaledAdam( + get_parameter_groups_with_lrs(model, lr=params.base_lr, include_names=True), + lr=params.base_lr, # should have no effect + clipping_scale=2.0, + ) + + scheduler = Eden(optimizer, params.lr_batches, params.lr_epochs) + + if checkpoints and "optimizer" in checkpoints: + logging.info("Loading optimizer state dict") + optimizer.load_state_dict(checkpoints["optimizer"]) + + if ( + checkpoints + and "scheduler" in checkpoints + and checkpoints["scheduler"] is not None + ): + logging.info("Loading scheduler state dict") + scheduler.load_state_dict(checkpoints["scheduler"]) + + if params.print_diagnostics: + opts = diagnostics.TensorDiagnosticOptions( + 2**22 + ) # allow 4 megabytes per sub-module + diagnostic = diagnostics.attach_diagnostics(model, opts) + + if params.inf_check: + register_inf_check_hooks(model) + + wenetspeech = WenetSpeechAsrDataModule(args) + + train_cuts = wenetspeech.train_cuts() + valid_cuts = wenetspeech.valid_cuts() + + def remove_short_and_long_utt(c: Cut): + # Keep only utterances with duration between 1 second and 15 seconds + # + # Caution: There is a reason to select 15.0 here. Please see + # ../local/display_manifest_statistics.py + # + # You should use ../local/display_manifest_statistics.py to get + # an utterance duration distribution for your dataset to select + # the threshold + if c.duration < 1.0 or c.duration > 15.0: + # logging.warning( + # f"Exclude cut with ID {c.id} from training. Duration: {c.duration}" + # ) + return False + + # In pruned RNN-T, we require that T >= S + # where T is the number of feature frames after subsampling + # and S is the number of tokens in the utterance + + # In ./zipformer.py, the conv module uses the following expression + # for subsampling + T = ((c.num_frames - 7) // 2 + 1) // 2 + tokens = graph_compiler.texts_to_ids([c.supervisions[0].text])[0] + + if T < len(tokens): + logging.warning( + f"Exclude cut with ID {c.id} from training. " + f"Number of frames (before subsampling): {c.num_frames}. " + f"Number of frames (after subsampling): {T}. " + f"Text: {c.supervisions[0].text}. " + f"Tokens: {tokens}. " + f"Number of tokens: {len(tokens)}" + ) + return False + + return True + + train_cuts = train_cuts.filter(remove_short_and_long_utt) + + if params.start_batch > 0 and checkpoints and "sampler" in checkpoints: + # We only load the sampler's state dict when it loads a checkpoint + # saved in the middle of an epoch + sampler_state_dict = checkpoints["sampler"] + else: + sampler_state_dict = None + + train_dl = wenetspeech.train_dataloaders( + train_cuts, sampler_state_dict=sampler_state_dict + ) + + valid_dl = wenetspeech.valid_dataloaders(valid_cuts) + + if False and not params.print_diagnostics: + scan_pessimistic_batches_for_oom( + model=model, + train_dl=train_dl, + optimizer=optimizer, + graph_compiler=graph_compiler, + params=params, + ) + + scaler = GradScaler(enabled=params.use_fp16, init_scale=1.0) + if checkpoints and "grad_scaler" in checkpoints: + logging.info("Loading grad scaler state dict") + scaler.load_state_dict(checkpoints["grad_scaler"]) + + for epoch in range(params.start_epoch, params.num_epochs + 1): + scheduler.step_epoch(epoch - 1) + fix_random_seed(params.seed + epoch - 1) + train_dl.sampler.set_epoch(epoch - 1) + + if tb_writer is not None: + tb_writer.add_scalar("train/epoch", epoch, params.batch_idx_train) + + params.cur_epoch = epoch + + train_one_epoch( + params=params, + model=model, + model_avg=model_avg, + optimizer=optimizer, + scheduler=scheduler, + graph_compiler=graph_compiler, + train_dl=train_dl, + valid_dl=valid_dl, + scaler=scaler, + tb_writer=tb_writer, + world_size=world_size, + rank=rank, + ) + + if params.print_diagnostics: + diagnostic.print_diagnostics() + break + + save_checkpoint( + params=params, + model=model, + model_avg=model_avg, + optimizer=optimizer, + scheduler=scheduler, + sampler=train_dl.sampler, + scaler=scaler, + rank=rank, + ) + + logging.info("Done!") + + if world_size > 1: + torch.distributed.barrier() + cleanup_dist() + + +def display_and_save_batch( + batch: dict, + params: AttributeDict, + graph_compiler: CharCtcTrainingGraphCompiler, +) -> None: + """Display the batch statistics and save the batch into disk. + + Args: + batch: + A batch of data. See `lhotse.dataset.K2SpeechRecognitionDataset()` + for the content in it. + params: + Parameters for training. See :func:`get_params`. + graph_compiler: + The compiler to encode texts to ids. + """ + from lhotse.utils import uuid4 + + filename = f"{params.exp_dir}/batch-{uuid4()}.pt" + logging.info(f"Saving batch to {filename}") + torch.save(batch, filename) + + supervisions = batch["supervisions"] + features = batch["inputs"] + + logging.info(f"features shape: {features.shape}") + + texts = supervisions["text"] + y = graph_compiler.texts_to_ids(texts) + num_tokens = sum(len(i) for i in y) + logging.info(f"num tokens: {num_tokens}") + + +def scan_pessimistic_batches_for_oom( + model: Union[nn.Module, DDP], + train_dl: torch.utils.data.DataLoader, + optimizer: torch.optim.Optimizer, + graph_compiler: CharCtcTrainingGraphCompiler, + params: AttributeDict, +): + from lhotse.dataset import find_pessimistic_batches + + logging.info( + "Sanity check -- see if any of the batches in epoch 1 would cause OOM." + ) + batches, crit_values = find_pessimistic_batches(train_dl.sampler) + for criterion, cuts in batches.items(): + batch = train_dl.dataset[cuts] + try: + with torch.cuda.amp.autocast(enabled=params.use_fp16): + loss, _ = compute_loss( + params=params, + model=model, + graph_compiler=graph_compiler, + batch=batch, + is_training=True, + ) + loss.backward() + optimizer.zero_grad() + except Exception as e: + if "CUDA out of memory" in str(e): + logging.error( + "Your GPU ran out of memory with the current " + "max_duration setting. We recommend decreasing " + "max_duration and trying again.\n" + f"Failing criterion: {criterion} " + f"(={crit_values[criterion]}) ..." + ) + display_and_save_batch(batch, params=params, graph_compiler=graph_compiler) + raise + logging.info( + f"Maximum memory allocated so far is {torch.cuda.max_memory_allocated()//1000000}MB" + ) + + +def main(): + parser = get_parser() + WenetSpeechAsrDataModule.add_arguments(parser) + args = parser.parse_args() + args.lang_dir = Path(args.lang_dir) + args.exp_dir = Path(args.exp_dir) + + world_size = args.world_size + assert world_size >= 1 + if world_size > 1: + mp.spawn(run, args=(world_size, args), nprocs=world_size, join=True) + else: + run(rank=0, world_size=1, args=args) + + +torch.set_num_threads(1) +torch.set_num_interop_threads(1) + +if __name__ == "__main__": + main() diff --git a/egs/wenetspeech/ASR/zipformer/zipformer.py b/egs/wenetspeech/ASR/zipformer/zipformer.py new file mode 120000 index 0000000000..23011dda71 --- /dev/null +++ b/egs/wenetspeech/ASR/zipformer/zipformer.py @@ -0,0 +1 @@ +../../../librispeech/ASR/zipformer/zipformer.py \ No newline at end of file