patrickvonplaten commited on
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Update README.md

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  1. README.md +15 -15
README.md CHANGED
@@ -21,7 +21,7 @@ model-index:
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  metrics:
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  - name: Test WER
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  type: wer
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- value: 48.314356
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  ---
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  # Wav2Vec2-Large-XLSR-53-Luganda
@@ -49,15 +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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@@ -85,30 +85,30 @@ processor = Wav2Vec2Processor.from_pretrained("birgermoell/wav2vec2-luganda")
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  model = Wav2Vec2ForCTC.from_pretrained("birgermoell/wav2vec2-luganda")
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  model.to("cuda")
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- chars_to_ignore_regex = '[\\\\\\\\\\\\\\\\,\\\\\\\\\\\\\\\\?\\\\\\\\\\\\\\\\.\\\\\\\\\\\\\\\\!\\\\\\\\\\\\\\\\-\\\\\\\\\\\\\\\\;\\\\\\\\\\\\\\\\:\\\\\\\\\\\\\\\\"\\\\\\\\\\\\\\\\“]'
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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)
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  metrics:
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  - name: Test WER
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  type: wer
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+ value: 48.31
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  ---
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  # Wav2Vec2-Large-XLSR-53-Luganda
 
49
  # 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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  model = Wav2Vec2ForCTC.from_pretrained("birgermoell/wav2vec2-luganda")
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  model.to("cuda")
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+ chars_to_ignore_regex = '[\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\,\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\?\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\.\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\!\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\-\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\;\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\:\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\"\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\“]'
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  resampler = torchaudio.transforms.Resample(48_000, 16_000)
90
 
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  # 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()
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+ \\\\\\\\\\\\\\\\treturn batch
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  test_dataset = test_dataset.map(speech_file_to_array_fn)
100
 
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  # Preprocessing the datasets.
102
  # We need to read the aduio files as arrays
103
  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)
114