update speechio whisper ft results

egs/multi_zh-hans/ASR/RESULTS.md

@@ -1,5 +1,48 @@
 ## Results
 
+### Multi Chinese datasets (without datatang 200h) finetuning results on Whisper-large-v2
+#### Whisper
+[./whisper](./whisper)
+
+Character Error Rates (CERs) listed below are produced by the checkpoint of the second epoch using greedy search.
+
+| Datasets | alimeeting | alimeeting | aishell-1 | aishell-1 | aishell-2 | aishell-2 | aishell-4 | magicdata | magicdata | kespeech-asr | kespeech-asr | kespeech-asr | WenetSpeech  |
+|--------------------------------|-------------------|--------------|----------------|-------------|------------------|-------------|------------------|------------------|-------------|-----------------------|-----------------------|-------------|-------------------|
+|  Split |           eval| test | dev | test | dev| test | test      | dev| test | dev phase1 | dev phase2 | test | test meeting |
+| Greedy Search |  23.22 | 28.24 | 0.61 | 0.66 | 2.67 | 2.80 | 16.61 | 2.56 | 2.21 | 4.73 | 1.90 | 5.98 |                    8.13 |
+
+Command for training is:
+```bash
+pip install -r whisper/requirements.txt
+
+# We updated the label of wenetspeech to remove OCR deletion errors, see https://github.com/wenet-e2e/WenetSpeech/discussions/54
+
+torchrun --nproc-per-node 8 ./whisper/train.py \
+  --max-duration 200 \
+  --exp-dir whisper/exp_large_v2 \
+  --model-name large-v2 \
+  --deepspeed \
+  --deepspeed_config ./whisper/ds_config_zero1.json
+```
+
+Command for decoding using fine-tuned models:
+```bash
+git lfs install
+git clone https://huggingface.co/yuekai/icefall_asr_multi-hans-zh_whisper
+ln -s icefall_asr_multi-hans-zh_whisper/v1.1/epoch-3-avg-10.pt whisper/exp_large_v2/epoch-999.pt
+
+python3 ./whisper/decode.py \
+  --exp-dir whisper/exp_large_v2 \
+  --model-name large-v2 \
+  --epoch 999 --avg 1 \
+  --beam-size 10 --max-duration 50
+```
+
+Fine-tuned models, training logs, decoding logs, tensorboard and decoding results
+are available at
+<https://huggingface.co/yuekai/icefall_asr_multi-hans-zh_whisper>
+
+
 ### Multi Chinese datasets char-based training results (Non-streaming) on zipformer model
 
 This is the [pull request #1238](https://github.com/k2-fsa/icefall/pull/1238) in icefall.

egs/multi_zh-hans/ASR/prepare.sh

@@ -226,8 +226,8 @@ if [ $stage -le 11 ] && [ $stop_stage -ge 11 ]; then
   log "Stage 11: Prepare WenetSpeech"
   if [ -e ../../wenetspeech/ASR/data/fbank/.preprocess_complete ]; then
     cd data/fbank
-    ln -svf $(realpath ../../../../wenetspeech/ASR/data/fbank/cuts_DEV.jsonl.gz) .
-    ln -svf $(realpath ../../../../wenetspeech/ASR/data/fbank/cuts_L.jsonl.gz) .
+    ln -svf $(realpath ../../../../wenetspeech/ASR/data/fbank/cuts_DEV_fixed.jsonl.gz) .
+    ln -svf $(realpath ../../../../wenetspeech/ASR/data/fbank/cuts_L_fixed.jsonl.gz) .
     ln -svf $(realpath ../../../../wenetspeech/ASR/data/fbank/cuts_TEST_MEETING.jsonl.gz) .
     ln -svf $(realpath ../../../../wenetspeech/ASR/data/fbank/cuts_TEST_NET.jsonl.gz) .
 

egs/multi_zh-hans/ASR/whisper/decode.py

@@ -57,6 +57,7 @@
 from multi_dataset import MultiDataset
 from tn.chinese.normalizer import Normalizer
 from whisper.normalizers import BasicTextNormalizer
+from whisper_decoder_forward_monkey_patch import replace_whisper_decoder_forward
 from whisper_encoder_forward_monkey_patch import replace_whisper_encoder_forward
 from zhconv import convert
 
@@ -214,7 +215,7 @@ def get_parser():
         "--model-name",
         type=str,
         default="large-v2",
-        choices=["large-v2", "large-v3", "medium", "small", "base", "tiny"],
+        choices=["large-v2", "large-v3", "medium", "base", "small", "tiny"],
         help="""The model name to use.
         """,
     )
@@ -226,6 +227,13 @@ def get_parser():
         help="replace whisper encoder forward method to remove input length restriction",
     )
 
+    parser.add_argument(
+        "--use-distill-whisper",
+        type=str2bool,
+        default=False,
+        help="Whether to use architecture of distill whisper.",
+    )
+
     return parser
 
 
@@ -307,6 +315,43 @@ def decode_dataset(
     Returns:
         Return a dict, whose key may be "beam-search".
     """
+
+    def normalize_text_alimeeting(text: str, normalize: str = "m2met") -> str:
+        """
+        Text normalization similar to M2MeT challenge baseline.
+        See: https://github.com/yufan-aslp/AliMeeting/blob/main/asr/local/text_normalize.pl
+        """
+        if normalize == "none":
+            return text
+        elif normalize == "m2met":
+            import re
+
+            text = text.replace(" ", "")
+            text = text.replace("<sil>", "")
+            text = text.replace("<%>", "")
+            text = text.replace("<->", "")
+            text = text.replace("<$>", "")
+            text = text.replace("<#>", "")
+            text = text.replace("<_>", "")
+            text = text.replace("<space>", "")
+            text = text.replace("`", "")
+            text = text.replace("&", "")
+            text = text.replace(",", "")
+            if re.search("[a-zA-Z]", text):
+                text = text.upper()
+            text = text.replace("Ａ", "A")
+            text = text.replace("ａ", "A")
+            text = text.replace("ｂ", "B")
+            text = text.replace("ｃ", "C")
+            text = text.replace("ｋ", "K")
+            text = text.replace("ｔ", "T")
+            text = text.replace("，", "")
+            text = text.replace("丶", "")
+            text = text.replace("。", "")
+            text = text.replace("、", "")
+            text = text.replace("？", "")
+            return text
+
     results = []
 
     num_cuts = 0
@@ -331,6 +376,7 @@ def decode_dataset(
             this_batch = []
             assert len(hyps) == len(texts)
             for cut_id, hyp_words, ref_text in zip(cut_ids, hyps, texts):
+                ref_text = normalize_text_alimeeting(ref_text)
                 ref_words = ref_text.split()
                 this_batch.append((cut_id, ref_words, hyp_words))
 
@@ -430,6 +476,8 @@ def main():
 
     if params.remove_whisper_encoder_input_length_restriction:
         replace_whisper_encoder_forward()
+    if params.use_distill_whisper:
+        replace_whisper_decoder_forward()
     model = whisper.load_model(params.model_name, "cpu")
     if params.epoch > 0:
         if params.avg > 1:

egs/multi_zh-hans/ASR/whisper/multi_dataset.py

@@ -43,7 +43,7 @@ def __init__(self, fbank_dir: str):
             - thchs_30_cuts_train.jsonl.gz
             - kespeech/kespeech-asr_cuts_train_phase1.jsonl.gz
             - kespeech/kespeech-asr_cuts_train_phase2.jsonl.gz
-            - wenetspeech/cuts_L.jsonl.gz
+            - wenetspeech/cuts_L_fixed.jsonl.gz
         """
         self.fbank_dir = Path(fbank_dir)
 
@@ -105,7 +105,7 @@ def train_cuts(self) -> CutSet:
         # WeNetSpeech
         logging.info("Loading WeNetSpeech in lazy mode")
         wenetspeech_L_cuts = load_manifest_lazy(
-            self.fbank_dir / "wenetspeech" / "cuts_L.jsonl.gz"
+            self.fbank_dir / "wenetspeech" / "cuts_L_fixed.jsonl.gz"
         )
 
         # KeSpeech
@@ -124,10 +124,10 @@ def train_cuts(self) -> CutSet:
             aishell_4_L_cuts,
             aishell_4_M_cuts,
             aishell_4_S_cuts,
+            alimeeting_cuts,
             stcmds_cuts,
             primewords_cuts,
             magicdata_cuts,
-            alimeeting_cuts,
             wenetspeech_L_cuts,
             kespeech_1_cuts,
             kespeech_2_cuts,
@@ -138,10 +138,10 @@ def train_cuts(self) -> CutSet:
                 len(aishell_4_L_cuts),
                 len(aishell_4_M_cuts),
                 len(aishell_4_S_cuts),
+                len(alimeeting_cuts),
                 len(stcmds_cuts),
                 len(primewords_cuts),
                 len(magicdata_cuts),
-                len(alimeeting_cuts),
                 len(wenetspeech_L_cuts),
                 len(kespeech_1_cuts),
                 len(kespeech_2_cuts),
@@ -151,55 +151,13 @@ def train_cuts(self) -> CutSet:
     def dev_cuts(self) -> CutSet:
         logging.info("About to get multidataset dev cuts")
 
-        # AISHELL
-        logging.info("Loading Aishell DEV set in lazy mode")
-        aishell_dev_cuts = load_manifest_lazy(
-            self.fbank_dir / "aishell_cuts_dev.jsonl.gz"
-        )
-
-        # AISHELL-2
-        logging.info("Loading Aishell-2 DEV set in lazy mode")
-        aishell2_dev_cuts = load_manifest_lazy(
-            self.fbank_dir / "aishell2_cuts_dev.jsonl.gz"
-        )
-
-        # Ali-Meeting
-        logging.info("Loading Ali-Meeting DEV set in lazy mode")
-        alimeeting_dev_cuts = load_manifest_lazy(
-            self.fbank_dir / "alimeeting-far_cuts_eval.jsonl.gz"
-        )
-
-        # MagicData
-        logging.info("Loading MagicData DEV set in lazy mode")
-        magicdata_dev_cuts = load_manifest_lazy(
-            self.fbank_dir / "magicdata_cuts_dev.jsonl.gz"
-        )
-
-        # KeSpeech
-        logging.info("Loading KeSpeech DEV set in lazy mode")
-        kespeech_dev_phase1_cuts = load_manifest_lazy(
-            self.fbank_dir / "kespeech" / "kespeech-asr_cuts_dev_phase1.jsonl.gz"
-        )
-        kespeech_dev_phase2_cuts = load_manifest_lazy(
-            self.fbank_dir / "kespeech" / "kespeech-asr_cuts_dev_phase2.jsonl.gz"
-        )
-
         # WeNetSpeech
         logging.info("Loading WeNetSpeech DEV set in lazy mode")
         wenetspeech_dev_cuts = load_manifest_lazy(
-            self.fbank_dir / "wenetspeech" / "cuts_DEV.jsonl.gz"
+            self.fbank_dir / "wenetspeech" / "cuts_DEV_fixed.jsonl.gz"
         )
 
         return wenetspeech_dev_cuts
-        # return [
-        #         aishell_dev_cuts,
-        #         aishell2_dev_cuts,
-        #         alimeeting_dev_cuts,
-        #         magicdata_dev_cuts,
-        #         kespeech_dev_phase1_cuts,
-        #         kespeech_dev_phase2_cuts,
-        #         wenetspeech_dev_cuts,
-        #     ]
 
     def test_cuts(self) -> Dict[str, CutSet]:
         logging.info("About to get multidataset test cuts")
@@ -267,30 +225,23 @@ def test_cuts(self) -> Dict[str, CutSet]:
             self.fbank_dir / "wenetspeech" / "cuts_TEST_NET.jsonl.gz"
         )
         wenetspeech_dev_cuts = load_manifest_lazy(
-            self.fbank_dir / "wenetspeech" / "cuts_DEV.jsonl.gz"
+            self.fbank_dir / "wenetspeech" / "cuts_DEV_fixed.jsonl.gz"
         )
 
         return {
-            "aishell-2_test": aishell2_test_cuts,
-            "aishell-4": aishell4_test_cuts,
-            "magicdata_test": magicdata_test_cuts,
-            "kespeech-asr_test": kespeech_test_cuts,
+            "wenetspeech-meeting_test": wenetspeech_test_meeting_cuts,
+            # "aishell_test": aishell_test_cuts,
+            # "aishell_dev": aishell_dev_cuts,
+            # "ali-meeting_test": alimeeting_test_cuts,
+            # "ali-meeting_eval": alimeeting_eval_cuts,
+            # "aishell-4_test": aishell4_test_cuts,
+            # "aishell-2_test": aishell2_test_cuts,
+            # "aishell-2_dev": aishell2_dev_cuts,
+            # "magicdata_test": magicdata_test_cuts,
+            # "magicdata_dev": magicdata_dev_cuts,
+            # "kespeech-asr_test": kespeech_test_cuts,
+            # "kespeech-asr_dev_phase1": kespeech_dev_phase1_cuts,
+            # "kespeech-asr_dev_phase2": kespeech_dev_phase2_cuts,
+            # "wenetspeech-net_test": wenetspeech_test_net_cuts,
+            # "wenetspeech_dev": wenetspeech_dev_cuts,
         }
-
-        # return {
-        #     "alimeeting_test": alimeeting_test_cuts,
-        #     "alimeeting_eval": alimeeting_eval_cuts,
-        #     "aishell_test": aishell_test_cuts,
-        #     "aishell_dev": aishell_dev_cuts,
-        #     "aishell-2_test": aishell2_test_cuts,
-        #     "aishell-2_dev": aishell2_dev_cuts,
-        #     "aishell-4": aishell4_test_cuts,
-        #     "magicdata_test": magicdata_test_cuts,
-        #     "magicdata_dev": magicdata_dev_cuts,
-        #     "kespeech-asr_test": kespeech_test_cuts,
-        #     "kespeech-asr_dev_phase1": kespeech_dev_phase1_cuts,
-        #     "kespeech-asr_dev_phase2": kespeech_dev_phase2_cuts,
-        #     "wenetspeech-meeting_test": wenetspeech_test_meeting_cuts,
-        #     "wenetspeech-net_test": wenetspeech_test_net_cuts,
-        #     "wenetspeech_dev": wenetspeech_dev_cuts,
-        # }

egs/multi_zh-hans/ASR/whisper/train.py

@@ -65,6 +65,7 @@
 from torch.nn.functional import pad as pad_tensor
 from torch.nn.parallel import DistributedDataParallel as DDP
 from torch.utils.tensorboard import SummaryWriter
+from whisper_decoder_forward_monkey_patch import replace_whisper_decoder_forward
 from whisper_encoder_forward_monkey_patch import replace_whisper_encoder_forward
 
 from icefall import diagnostics
@@ -146,7 +147,7 @@ def get_parser():
         "--model-name",
         type=str,
         default="large-v2",
-        choices=["large-v2", "large-v3", "medium", "small", "base", "tiny"],
+        choices=["large-v2", "large-v3", "medium", "base", "small", "tiny"],
         help="""The model name to use.
         """,
     )
@@ -232,6 +233,13 @@ def get_parser():
         help="Whether to use half precision training.",
     )
 
+    parser.add_argument(
+        "--use-distill-whisper",
+        type=str2bool,
+        default=False,
+        help="Whether to use architecture of distill whisper.",
+    )
+
     parser = deepspeed.add_config_arguments(parser)
 
     return parser
@@ -441,6 +449,42 @@ def _batch_tensors(tensors: List[Tensor], pad_value: Any) -> Tensor:
             padded_tensors.append(pad_tensor(tensor, padding, "constant", pad_value))
         return torch.stack([tensor for tensor in padded_tensors], dim=0)
 
+    def normalize_text_alimeeting(text: str, normalize: str = "m2met") -> str:
+        """
+        Text normalization similar to M2MeT challenge baseline.
+        See: https://github.com/yufan-aslp/AliMeeting/blob/main/asr/local/text_normalize.pl
+        """
+        if normalize == "none":
+            return text
+        elif normalize == "m2met":
+            import re
+
+            text = text.replace(" ", "")
+            text = text.replace("<sil>", "")
+            text = text.replace("<%>", "")
+            text = text.replace("<->", "")
+            text = text.replace("<$>", "")
+            text = text.replace("<#>", "")
+            text = text.replace("<_>", "")
+            text = text.replace("<space>", "")
+            text = text.replace("`", "")
+            text = text.replace("&", "")
+            text = text.replace(",", "")
+            if re.search("[a-zA-Z]", text):
+                text = text.upper()
+            text = text.replace("Ａ", "A")
+            text = text.replace("ａ", "A")
+            text = text.replace("ｂ", "B")
+            text = text.replace("ｃ", "C")
+            text = text.replace("ｋ", "K")
+            text = text.replace("ｔ", "T")
+            text = text.replace("，", "")
+            text = text.replace("丶", "")
+            text = text.replace("。", "")
+            text = text.replace("、", "")
+            text = text.replace("？", "")
+            return text
+
     max_frames = params.max_duration * 1000 // params.frame_shift_ms
     allowed_max_frames = int(max_frames * (1.0 + params.allowed_excess_duration_ratio))
     batch = filter_uneven_sized_batch(batch, allowed_max_frames)
@@ -459,7 +503,7 @@ def _batch_tensors(tensors: List[Tensor], pad_value: Any) -> Tensor:
 
     texts = batch["supervisions"]["text"]
     # remove spaces in texts
-    texts = [text.replace(" ", "") for text in texts]
+    texts = [normalize_text_alimeeting(text) for text in texts]
 
     text_tokens_list = [
         list(tokenizer.sot_sequence_including_notimestamps)
@@ -759,6 +803,8 @@ def run(rank, world_size, args):
     logging.info("About to create model")
 
     replace_whisper_encoder_forward()
+    if params.use_distill_whisper:
+        replace_whisper_decoder_forward()
     model = whisper.load_model(params.model_name, "cpu")
     del model.alignment_heads