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[Examples] Generalise run audio classification for log-mel models (#21756)

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* [Examples] Generalise run audio classification for log-mel models

* batch feature extractor

* make style
This commit is contained in:
Sanchit Gandhi
2023-02-24 09:19:07 +01:00
committed by GitHub
parent f7ca656f07
commit 13489248fa
1 changed files with 9 additions and 6 deletions
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15
examples/pytorch/audio-classification/run_audio_classification.py
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@@ -289,24 +289,27 @@ def main():
data_args.audio_column_name, datasets.features.Audio(sampling_rate=feature_extractor.sampling_rate) data_args.audio_column_name, datasets.features.Audio(sampling_rate=feature_extractor.sampling_rate)
) )
model_input_name = feature_extractor.model_input_names[0]
def train_transforms(batch): def train_transforms(batch):
"""Apply train_transforms across a batch.""" """Apply train_transforms across a batch."""
output_batch = {"input_values": []} subsampled_wavs = []
for audio in batch[data_args.audio_column_name]: for audio in batch[data_args.audio_column_name]:
wav = random_subsample( wav = random_subsample(
audio["array"], max_length=data_args.max_length_seconds, sample_rate=feature_extractor.sampling_rate audio["array"], max_length=data_args.max_length_seconds, sample_rate=feature_extractor.sampling_rate
) )
output_batch["input_values"].append(wav) subsampled_wavs.append(wav)
inputs = feature_extractor(subsampled_wavs, sampling_rate=feature_extractor.sampling_rate)
output_batch = {model_input_name: inputs.get(model_input_name)}
output_batch["labels"] = list(batch[data_args.label_column_name]) output_batch["labels"] = list(batch[data_args.label_column_name])
return output_batch return output_batch
def val_transforms(batch): def val_transforms(batch):
"""Apply val_transforms across a batch.""" """Apply val_transforms across a batch."""
output_batch = {"input_values": []} wavs = [audio["array"] for audio in batch[data_args.audio_column_name]]
for audio in batch[data_args.audio_column_name]: inputs = feature_extractor(wavs, sampling_rate=feature_extractor.sampling_rate)
wav = audio["array"] output_batch = {model_input_name: inputs.get(model_input_name)}
output_batch["input_values"].append(wav)
output_batch["labels"] = list(batch[data_args.label_column_name]) output_batch["labels"] = list(batch[data_args.label_column_name])
return output_batch return output_batch