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Update README.md

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  1. README.md +18 -20
README.md CHANGED
@@ -2,8 +2,6 @@
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  language: mn
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  datasets:
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  - common_voice
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- metrics:
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- - wer
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  tags:
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  - audio
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  - automatic-speech-recognition
@@ -11,15 +9,15 @@ tags:
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  - xlsr-fine-tuning-week
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  license: apache-2.0
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  model-index:
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- - name: bayartsogt/wav2vec2-large-xlsr-mongolian
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- results:
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  - task:
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  name: Speech Recognition
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  type: automatic-speech-recognition
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  dataset:
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- name: Common Voice mn-MN
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  type: common_voice
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- args: mn
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  metrics:
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  - name: Test WER
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  type: wer
@@ -51,15 +49,15 @@ resampler = torchaudio.transforms.Resample(48_000, 16_000)
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  # Preprocessing the datasets.
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  # We need to read the aduio files as arrays
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  def speech_file_to_array_fn(batch):
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- \tspeech_array, sampling_rate = torchaudio.load(batch["path"])
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- \tbatch["speech"] = resampler(speech_array).squeeze().numpy()
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- \treturn batch
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  test_dataset = test_dataset.map(speech_file_to_array_fn)
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  inputs = processor(test_dataset["speech"][:2], sampling_rate=16_000, return_tensors="pt", padding=True)
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  with torch.no_grad():
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- \tlogits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits
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  predicted_ids = torch.argmax(logits, dim=-1)
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@@ -86,31 +84,31 @@ processor = Wav2Vec2Processor.from_pretrained("bayartsogt/wav2vec2-large-xlsr-mo
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  model = Wav2Vec2ForCTC.from_pretrained("bayartsogt/wav2vec2-large-xlsr-mongolian")
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  model.to("cuda")
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- chars_to_ignore_regex = '[\\,\\?\\.\\!\\-\\;\\:\\"\\“\\%\\‘\\”\\�\\'h\\«\\»]'
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  resampler = torchaudio.transforms.Resample(48_000, 16_000)
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  # Preprocessing the datasets.
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  # We need to read the aduio files as arrays
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  def speech_file_to_array_fn(batch):
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- \tbatch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower()
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- \tspeech_array, sampling_rate = torchaudio.load(batch["path"])
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- \tbatch["speech"] = resampler(speech_array).squeeze().numpy()
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- \treturn batch
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  test_dataset = test_dataset.map(speech_file_to_array_fn)
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  # Preprocessing the datasets.
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  # We need to read the aduio files as arrays
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  def evaluate(batch):
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- \tinputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)
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- \twith torch.no_grad():
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- \t\tlogits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits
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  pred_ids = torch.argmax(logits, dim=-1)
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- \tbatch["pred_strings"] = processor.batch_decode(pred_ids)
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- \treturn batch
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  result = test_dataset.map(evaluate, batched=True, batch_size=8)
116
 
 
2
  language: mn
3
  datasets:
4
  - common_voice
 
 
5
  tags:
6
  - audio
7
  - automatic-speech-recognition
 
9
  - xlsr-fine-tuning-week
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  license: apache-2.0
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  model-index:
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+ - name: XLSR Wav2Vec2 Mongolian by Bayartsogt
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+ results:
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  - task:
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  name: Speech Recognition
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  type: automatic-speech-recognition
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  dataset:
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+ name: Common Voice mn
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  type: common_voice
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+ args: mn
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  metrics:
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  - name: Test WER
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  type: wer
 
49
  # Preprocessing the datasets.
50
  # We need to read the aduio files as arrays
51
  def speech_file_to_array_fn(batch):
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+ \\tspeech_array, sampling_rate = torchaudio.load(batch["path"])
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+ \\tbatch["speech"] = resampler(speech_array).squeeze().numpy()
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+ \\treturn batch
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  test_dataset = test_dataset.map(speech_file_to_array_fn)
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  inputs = processor(test_dataset["speech"][:2], sampling_rate=16_000, return_tensors="pt", padding=True)
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  with torch.no_grad():
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+ \\tlogits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits
61
 
62
  predicted_ids = torch.argmax(logits, dim=-1)
63
 
 
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  model = Wav2Vec2ForCTC.from_pretrained("bayartsogt/wav2vec2-large-xlsr-mongolian")
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  model.to("cuda")
86
 
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+ chars_to_ignore_regex = '[\\\\,\\\\?\\\\.\\\\!\\\\-\\\\;\\\\:\\\\"\\\\“\\\\%\\\\‘\\\\”\\\\�\\\\'h\\\\«\\\\»]'
88
 
89
  resampler = torchaudio.transforms.Resample(48_000, 16_000)
90
 
91
  # Preprocessing the datasets.
92
  # We need to read the aduio files as arrays
93
  def speech_file_to_array_fn(batch):
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+ \\tbatch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower()
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+ \\tspeech_array, sampling_rate = torchaudio.load(batch["path"])
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+ \\tbatch["speech"] = resampler(speech_array).squeeze().numpy()
97
+ \\treturn batch
98
 
99
  test_dataset = test_dataset.map(speech_file_to_array_fn)
100
 
101
  # Preprocessing the datasets.
102
  # We need to read the aduio files as arrays
103
  def evaluate(batch):
104
+ \\tinputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)
105
 
106
+ \\twith torch.no_grad():
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+ \\t\\tlogits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits
108
 
109
  pred_ids = torch.argmax(logits, dim=-1)
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+ \\tbatch["pred_strings"] = processor.batch_decode(pred_ids)
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+ \\treturn batch
112
 
113
  result = test_dataset.map(evaluate, batched=True, batch_size=8)
114