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Merged
regisss merged 1 commit into from
Feb 24, 2023
Merged

Update audio classification example #171

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46 changes: 23 additions & 23 deletions examples/audio-classification/run_audio_classification.py
View file
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Original file line number Diff line number Diff line change
Expand Up @@ -275,42 +275,42 @@ def main():
# Max input length
max_length = int(round(feature_extractor.sampling_rate * data_args.max_length_seconds))

model_input_name = feature_extractor.model_input_names[0]

def train_transforms(batch):
"""Apply train_transforms across a batch."""
output_batch = {"input_values": []}
subsampled_wavs = []

for audio in batch[data_args.audio_column_name]:
wav = random_subsample(
audio["array"], max_length=data_args.max_length_seconds, sample_rate=feature_extractor.sampling_rate
)
preprocessed_audio = feature_extractor(
wav,
max_length=max_length,
sampling_rate=feature_extractor.sampling_rate,
padding="max_length",
truncation=True,
)
output_batch["input_values"].append(preprocessed_audio["input_values"][0])

subsampled_wavs.append(wav)
inputs = feature_extractor(
subsampled_wavs,
max_length=max_length,
sampling_rate=feature_extractor.sampling_rate,
padding="max_length",
truncation=True,
)
output_batch = {model_input_name: inputs.get(model_input_name)}
output_batch["labels"] = list(batch[data_args.label_column_name])

return output_batch

def val_transforms(batch):
"""Apply val_transforms across a batch."""
output_batch = {"input_values": []}

for audio in batch[data_args.audio_column_name]:
wav = audio["array"]
preprocessed_audio = feature_extractor(
wav,
max_length=max_length,
sampling_rate=feature_extractor.sampling_rate,
padding="max_length",
truncation=True,
)
output_batch["input_values"].append(preprocessed_audio["input_values"][0])

wavs = [audio["array"] for audio in batch[data_args.audio_column_name]]
inputs = feature_extractor(
wavs,
max_length=max_length,
sampling_rate=feature_extractor.sampling_rate,
padding="max_length",
truncation=True,
)
output_batch = {model_input_name: inputs.get(model_input_name)}
output_batch["labels"] = list(batch[data_args.label_column_name])

return output_batch

# Prepare label mappings.
Expand Down
52 changes: 21 additions & 31 deletions tests/example_diff/run_audio_classification.txt
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Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -67,41 +67,31 @@
> # Max input length
> max_length = int(round(feature_extractor.sampling_rate * data_args.max_length_seconds))
>
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>
299,300c286,293
< output_batch["input_values"].append(wav)
< output_batch["labels"] = list(batch[data_args.label_column_name])
302c289,295
< inputs = feature_extractor(subsampled_wavs, sampling_rate=feature_extractor.sampling_rate)
---
> preprocessed_audio = feature_extractor(
> wav,
> max_length=max_length,
> sampling_rate=feature_extractor.sampling_rate,
> padding="max_length",
> truncation=True,
> )
> output_batch["input_values"].append(preprocessed_audio["input_values"][0])
301a295
> output_batch["labels"] = list(batch[data_args.label_column_name])
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>
309,310c304,311
< output_batch["input_values"].append(wav)
< output_batch["labels"] = list(batch[data_args.label_column_name])
> inputs = feature_extractor(
> subsampled_wavs,
> max_length=max_length,
> sampling_rate=feature_extractor.sampling_rate,
> padding="max_length",
> truncation=True,
> )
311c304,310
< inputs = feature_extractor(wavs, sampling_rate=feature_extractor.sampling_rate)
---
> preprocessed_audio = feature_extractor(
> wav,
> max_length=max_length,
> sampling_rate=feature_extractor.sampling_rate,
> padding="max_length",
> truncation=True,
> )
> output_batch["input_values"].append(preprocessed_audio["input_values"][0])
311a313
> output_batch["labels"] = list(batch[data_args.label_column_name])
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> inputs = feature_extractor(
> wavs,
> max_length=max_length,
> sampling_rate=feature_extractor.sampling_rate,
> padding="max_length",
> truncation=True,
> )
376c375
< trainer = Trainer(
---
> trainer = GaudiTrainer(
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377a377
> gaudi_config=gaudi_config,