api/docs modifications

Author: Caroline Chen

docs/source/prototype.rst

@@ -65,6 +65,18 @@ Hypothesis
 .. autoclass:: Hypothesis
 
 
+KenLMLexiconDecoder
+~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: KenLMLexiconDecoder
+
+
+kenlm_lexicon_decoder
+~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: kenlm_lexicon_decoder
+
+
 References
 ~~~~~~~~~~
 

torchaudio/csrc/decoder/README.md

@@ -1,11 +1,11 @@
 # Flashlight Decoder Binding
 CTC Decoder with KenLM and lexicon support based on [flashlight](https://github.com/flashlight/flashlight) decoder implementation
-and fairseq [KenLMDecoder](https://github.com/pytorch/fairseq/blob/fcca32258c8e8bcc9f9890bf4714fa2f96b6b3e1/examples/speech_recognition/new/decoders/flashlight_decoder.py#L53) 
+and fairseq [KenLMDecoder](https://github.com/pytorch/fairseq/blob/fcca32258c8e8bcc9f9890bf4714fa2f96b6b3e1/examples/speech_recognition/new/decoders/flashlight_decoder.py#L53)
 Python wrapper
 
 ## Setup
 ### Build KenLM
-- Install KenLM following the instructions [here](https://github.com/kpu/kenlm#compiling) 
+- Install KenLM in your audio directory following the instructions [here](https://github.com/kpu/kenlm#compiling)
 - set `KENLM_ROOT` variable to the KenLM installation path
 ### Build torchaudio with decoder support
 ```
@@ -17,7 +17,7 @@ BUILD_CTC_DECODER=1 python setup.py develop
 from torchaudio.prototype import kenlm_lexicon_decoder
 decoder = kenlm_lexicon_decoder(args...)
 results = decoder(emissions) # dim (B, nbest) of dictionary of "tokens", "score", "words" keys
-best_transcript = " ".join(results[0][0]["words"]).strip()
+best_transcripts = [" ".join(results[i][0].words).strip() for i in range(B)]
 ```
 
 ## Required Files
@@ -26,11 +26,11 @@ best_transcript = " ".join(results[0][0]["words"]).strip()
 - language model: n-gram KenLM model
 
 ## Experiment Results
-LibriSpeech dev-other and test-other results using pretrained [Wav2Vec2](https://arxiv.org/pdf/2006.11477.pdf) models of 
-BASE configuration. 
+LibriSpeech dev-other and test-other results using pretrained [Wav2Vec2](https://arxiv.org/pdf/2006.11477.pdf) models of
+BASE configuration.
 
 | Model       | Decoder    | dev-other   | test-other | beam search params                          |
-| ----------- | ---------- | ----------- | ---------- | ------------------------------------------- |
+| ----------- | ---------- | ----------- | ---------- |-------------------------------------------- |
 | BASE_10M    | Greedy     | 51.6        | 51         |                                             |
 |             | 4-gram LM  | 15.95       | 15.9       | LM weight=3.23, word score=-0.26, beam=1500 |
 | BASE_100H   | Greedy     | 13.6        | 13.3       |                                             |

torchaudio/prototype/__init__.py

@@ -1,6 +1,7 @@
 from .emformer import Emformer
 from .rnnt import RNNT, emformer_rnnt_base, emformer_rnnt_model
 from .rnnt_decoder import Hypothesis, RNNTBeamSearch
+from .ctc_decoder import kenlm_lexicon_decoder
 
 
 __all__ = [
@@ -10,4 +11,6 @@
     "RNNTBeamSearch",
     "emformer_rnnt_base",
     "emformer_rnnt_model",
+    "KenLMLexiconDecoder",
+    "kenlm_lexicon_decoder",
 ]

torchaudio/prototype/ctc_decoder.py

@@ -1,6 +1,7 @@
 import torch
 import itertools as it
 from typing import List, Optional, Dict
+from collections import namedtuple
 
 import torchaudio
 
@@ -26,6 +27,8 @@
 __all__ = ["KenLMLexiconDecoder", "kenlm_lexicon_decoder"]
 
 
+Hypothesis = namedtuple("Hypothesis", ["tokens", "words", "score"])
+
 class KenLMLexiconDecoder:
     def __init__(
         self,
@@ -38,9 +41,11 @@ def __init__(
         blank_token: str,
         sil_token: str,
     ) -> None:
-
         """
-        Construct a KenLM CTC Lexcion Decoder.
+        KenLM CTC Decoder with Lexicon constraint.
+
+        Note:
+            To build the decoder, please use the factory function kenlm_lexicon_decoder.
 
         Args:
             nbest (int): number of best decodings to return
@@ -107,13 +112,13 @@ def decode(
                 in time axis of the output Tensor in each batch
 
         Returns:
-            List[List[Dict]]:
+            List[Hypothesis]:
                 List of sorted best hypotheses for each audio sequence in the batch.
 
-                Each hypothesis is dictionary with the following mapping:
-                    "tokens": torch.LongTensor of raw token IDs
-                    "score": hypothesis score
-                    "words": list of decoded words
+                Each hypothesis is named tuple with the following fields:
+                    tokens: torch.LongTensor of raw token IDs
+                    score: hypothesis score
+                    words: list of decoded words
         """
         B, T, N = emissions.size()
         if lengths is None:
@@ -128,13 +133,12 @@ def decode(
             nbest_results = results[: self.nbest]
             hypos.append(
                 [
-                    {
-                        "tokens": self._get_tokens(result.tokens),
-                        "score": result.score,
-                        "words": [
-                            self.word_dict.get_entry(x) for x in result.words if x >= 0
-                        ]
-                    }
+                    Hypothesis(
+                        self._get_tokens(result.tokens),
+                        list(self.word_dict.get_entry(x) for x in result.words if x >= 0),
+                        result.score,
+                        
+                    )
                     for result in nbest_results
                 ]
             )