[BC-breaking] Remove unused dimension from pretrained Wav2Vec2 ASR (#1914)

* [BC-breaking] Remove unused dimension from pretrained Wav2Vec2 ASR

The Wav2Vec2 ASR pretrained weights originated from fairseq have
extra dimension that have nothing to do with the ASR task.

https://github.com/pytorch/fairseq/blob/c5ff181125c7e6126b49a85e5ebdd5f5b6a07914/fairseq/data/dictionary.py#L18-L37

which is masked during the loss computation as

https://github.com/pytorch/fairseq/blob/c5ff181125c7e6126b49a85e5ebdd5f5b6a07914/fairseq/criterions/ctc.py#L126-L128

This change removes it.

* Use '-' for blank token representation.

test/integration_tests/conftest.py

@@ -4,8 +4,9 @@ import pytest
 
 
 class GreedyCTCDecoder(torch.nn.Module):
-    def __init__(self, labels):
+    def __init__(self, labels, blank: int = 0):
         super().__init__()
+        self.blank = blank
         self.labels = labels
 
     def forward(self, logits: torch.Tensor) -> str:
@@ -21,9 +22,8 @@ class GreedyCTCDecoder(torch.nn.Module):
         best_path = torch.unique_consecutive(best_path, dim=-1)
         hypothesis = []
         for i in best_path:
-            char = self.labels[i]
-            if char not in ['<s>', '<pad>']:
-                hypothesis.append(char)
+            if i != self.blank:
+                hypothesis.append(self.labels[i])
         return ''.join(hypothesis)
 
 

torchaudio/pipelines/_wav2vec2.py

 from dataclasses import dataclass
 from typing import Dict, Tuple, Any
 
+import torch
 from torch.hub import load_state_dict_from_url
 
 from torchaudio.models import wav2vec2_model, Wav2Vec2Model
@@ -68,6 +69,14 @@ class Wav2Vec2Bundle:
         url = f'https://download.pytorch.org/torchaudio/models/{self._path}'
         dl_kwargs = {} if dl_kwargs is None else dl_kwargs
         state_dict = load_state_dict_from_url(url, **dl_kwargs)
+
+        if model.aux is not None:
+            # For ASR task, the parameter originated from fairseq has unrelated dimensions at index 1, 2, 3
+            # It's originated from fairseq but not used, so we remove it here.
+            # https://github.com/pytorch/fairseq/blob/c5ff181125c7e6126b49a85e5ebdd5f5b6a07914/fairseq/data/dictionary.py#L21-L37
+            for key in ['aux.weight', 'aux.bias']:
+                t = state_dict[key]
+                state_dict[key] = torch.stack([t[i] for i in range(t.size(0)) if i not in (1, 2, 3)])
         model.load_state_dict(state_dict)
         model.eval()
         return model
@@ -102,7 +111,7 @@ class Wav2Vec2ASRBundle(Wav2Vec2Bundle):
         >>> # Check the corresponding labels of the output.
         >>> labels = bundle.get_labels()
         >>> print(labels)
-        ('<s>', '<pad>', '</s>', '<unk>', '|', 'E', 'T', 'A', 'O', 'N', 'I', 'H', 'S', 'R', 'D', 'L', 'U', 'M', 'W', 'C', 'F', 'G', 'Y', 'P', 'B', 'V', 'K', "'", 'X', 'J', 'Q', 'Z')
+        ('-', '|', 'E', 'T', 'A', 'O', 'N', 'I', 'H', 'S', 'R', 'D', 'L', 'U', 'M', 'W', 'C', 'F', 'G', 'Y', 'P', 'B', 'V', 'K', "'", 'X', 'J', 'Q', 'Z')
         >>>
         >>> # Resample audio to the expected sampling rate
         >>> waveform = torchaudio.functional.resample(waveform, sample_rate, bundle.sample_rate)
@@ -119,20 +128,14 @@ class Wav2Vec2ASRBundle(Wav2Vec2Bundle):
     def get_labels(
             self,
             *,
-            bos: str = '<s>',
-            pad: str = '<pad>',
-            eos: str = '</s>',
-            unk: str = '<unk>',
+            blank: str = '-',
     ) -> Tuple[str]:
         """The output class labels (only applicable to fine-tuned bundles)
 
-        The first four tokens are BOS, padding, EOS and UNK tokens and they can be customized.
+        The first is blank token, and it is customizable.
 
         Args:
-            bos (str, optional): Beginning of sentence token. (default: ``'<s>'``)
-            pad (str, optional): Padding token. (default: ``'<pad>'``)
-            eos (str, optional): End of sentence token. (default: ``'</s>'``)
-            unk (str, optional): Token for unknown class. (default: ``'<unk>'``)
+            blank (str, optional): Blank token. (default: ``'-'``)
 
         Returns:
             Tuple[str]:
@@ -142,11 +145,9 @@ class Wav2Vec2ASRBundle(Wav2Vec2Bundle):
         Example
             >>> import torchaudio
             >>> torchaudio.models.HUBERT_ASR_LARGE.get_labels()
-            ('<s>', '<pad>', '</s>', '<unk>', '|', 'E', 'T', 'A', 'O', 'N', 'I', 'H', 'S', 'R', 'D', 'L', 'U', 'M', 'W', 'C', 'F', 'G', 'Y', 'P', 'B', 'V', 'K', "'", 'X', 'J', 'Q', 'Z')
+            ('-', '|', 'E', 'T', 'A', 'O', 'N', 'I', 'H', 'S', 'R', 'D', 'L', 'U', 'M', 'W', 'C', 'F', 'G', 'Y', 'P', 'B', 'V', 'K', "'", 'X', 'J', 'Q', 'Z')
         """  # noqa: E501
-        if self._labels is None:
-            raise ValueError('Pre-trained models do not have labels.')
-        return (bos, pad, eos, unk, *self._labels)
+        return (blank, *self._labels)
 
 
 def _get_labels():
@@ -252,7 +253,7 @@ WAV2VEC2_ASR_BASE_10M = Wav2Vec2ASRBundle(
         'encoder_dropout': 0.1,
         'encoder_layer_norm_first': False,
         'encoder_layer_drop': 0.05,
-        "aux_num_out": 32,
+        "aux_num_out": 29,
     },
     _labels=_get_labels(),
     _sample_rate=16000,
@@ -298,7 +299,7 @@ WAV2VEC2_ASR_BASE_100H = Wav2Vec2ASRBundle(
         'encoder_dropout': 0.1,
         'encoder_layer_norm_first': False,
         'encoder_layer_drop': 0.05,
-        "aux_num_out": 32,
+        "aux_num_out": 29,
     },
     _labels=_get_labels(),
     _sample_rate=16000,
@@ -344,7 +345,7 @@ WAV2VEC2_ASR_BASE_960H = Wav2Vec2ASRBundle(
         "encoder_dropout": 0.1,
         "encoder_layer_norm_first": False,
         "encoder_layer_drop": 0.05,
-        "aux_num_out": 32,
+        "aux_num_out": 29,
     },
     _labels=_get_labels(),
     _sample_rate=16000,
@@ -433,7 +434,7 @@ WAV2VEC2_ASR_LARGE_10M = Wav2Vec2ASRBundle(
         "encoder_dropout": 0.0,
         "encoder_layer_norm_first": False,
         "encoder_layer_drop": 0.2,
-        "aux_num_out": 32,
+        "aux_num_out": 29,
     },
     _labels=_get_labels(),
     _sample_rate=16000,
@@ -479,7 +480,7 @@ WAV2VEC2_ASR_LARGE_100H = Wav2Vec2ASRBundle(
         "encoder_dropout": 0.0,
         "encoder_layer_norm_first": False,
         "encoder_layer_drop": 0.2,
-        "aux_num_out": 32,
+        "aux_num_out": 29,
     },
     _labels=_get_labels(),
     _sample_rate=16000,
@@ -525,7 +526,7 @@ WAV2VEC2_ASR_LARGE_960H = Wav2Vec2ASRBundle(
         "encoder_dropout": 0.0,
         "encoder_layer_norm_first": False,
         "encoder_layer_drop": 0.2,
-        "aux_num_out": 32,
+        "aux_num_out": 29,
     },
     _labels=_get_labels(),
     _sample_rate=16000,
@@ -614,7 +615,7 @@ WAV2VEC2_ASR_LARGE_LV60K_10M = Wav2Vec2ASRBundle(
         "encoder_dropout": 0.0,
         "encoder_layer_norm_first": True,
         "encoder_layer_drop": 0.0,
-        "aux_num_out": 32,
+        "aux_num_out": 29,
     },
     _labels=_get_labels(),
     _sample_rate=16000,
@@ -660,7 +661,7 @@ WAV2VEC2_ASR_LARGE_LV60K_100H = Wav2Vec2ASRBundle(
         "encoder_dropout": 0.0,
         "encoder_layer_norm_first": True,
         "encoder_layer_drop": 0.0,
-        "aux_num_out": 32,
+        "aux_num_out": 29,
     },
     _labels=_get_labels(),
     _sample_rate=16000,
@@ -706,7 +707,7 @@ WAV2VEC2_ASR_LARGE_LV60K_960H = Wav2Vec2ASRBundle(
         "encoder_dropout": 0.0,
         "encoder_layer_norm_first": True,
         "encoder_layer_drop": 0.0,
-        "aux_num_out": 32,
+        "aux_num_out": 29,
     },
     _labels=_get_labels(),
     _sample_rate=16000,
@@ -932,7 +933,7 @@ HUBERT_ASR_LARGE = Wav2Vec2ASRBundle(
         'encoder_dropout': 0.0,
         'encoder_layer_norm_first': True,
         'encoder_layer_drop': 0.1,
-        'aux_num_out': 32,
+        'aux_num_out': 29,
     },
     _labels=_get_labels(),
     _sample_rate=16000,
@@ -979,7 +980,7 @@ HUBERT_ASR_XLARGE = Wav2Vec2ASRBundle(
         'encoder_dropout': 0.0,
         'encoder_layer_norm_first': True,
         'encoder_layer_drop': 0.1,
-        'aux_num_out': 32,
+        'aux_num_out': 29,
     },
     _labels=_get_labels(),
     _sample_rate=16000,