init
Browse files- download_audio.py +0 -1
- main.sh +2 -2
download_audio.py
CHANGED
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@@ -125,7 +125,6 @@ def get_audio(dataframe: pd.DataFrame):
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| 125 |
wav, sr = sf.read(features[f"{side}.path"])
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| 126 |
if wav.ndim > 1:
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wav = wav[:, 0]
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| 128 |
-
# wav = audio_loader.decode_example({"path": features[f"{side}.path"], "bytes": None})
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| 129 |
if sr != sampling_rate:
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print(f"RESAMPLING: {wav.shape} length audio")
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if torch.cuda.is_available():
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wav, sr = sf.read(features[f"{side}.path"])
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| 126 |
if wav.ndim > 1:
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| 127 |
wav = wav[:, 0]
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| 128 |
if sr != sampling_rate:
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print(f"RESAMPLING: {wav.shape} length audio")
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if torch.cuda.is_available():
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main.sh
CHANGED
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@@ -23,7 +23,7 @@ python -c 'file_name="tmp.mp3"; from datasets import Audio; a=Audio(); wav=a.dec
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####################
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export CUDA_VISIBLE_DEVICES=
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export N_POOL=1
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-
export DATASET_ID=
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export DIRECTION="enA-jaA"
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export LINE_NO_START=$(((DATASET_ID-1) * 2500))
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export LINE_NO_END=$((DATASET_ID * 2500))
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@@ -31,7 +31,7 @@ echo ${LINE_NO_START}
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python download_audio.py
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export N_POOL=10
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-
export DATASET_ID=
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export DIRECTION="enA-jaA"
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export LINE_NO_START=$(((DATASET_ID-1) * 2500))
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export LINE_NO_END=$((DATASET_ID * 2500))
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####################
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export CUDA_VISIBLE_DEVICES=
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export N_POOL=1
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+
export DATASET_ID=1
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export DIRECTION="enA-jaA"
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export LINE_NO_START=$(((DATASET_ID-1) * 2500))
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export LINE_NO_END=$((DATASET_ID * 2500))
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python download_audio.py
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export N_POOL=10
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+
export DATASET_ID=2
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export DIRECTION="enA-jaA"
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export LINE_NO_START=$(((DATASET_ID-1) * 2500))
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export LINE_NO_END=$((DATASET_ID * 2500))
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