up (#13733)
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@@ -81,7 +81,7 @@ of **0.35**.
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The following command shows how to fine-tune [XLSR-Wav2Vec2](https://huggingface.co/transformers/master/model_doc/xlsr_wav2vec2.html) on [Common Voice](https://huggingface.co/datasets/common_voice) using 8 GPUs in half-precision.
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```bash
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python -m torch.distributed.launch \
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OMP_NUM_THREADS=1 python -m torch.distributed.launch \
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--nproc_per_node 8 run_speech_recognition_ctc.py \
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--dataset_name="common_voice" \
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--model_name_or_path="facebook/wav2vec2-large-xlsr-53" \
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@@ -103,12 +103,6 @@ class ModelArguments:
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"vectors will be masked along the time axis. This is only relevant if ``apply_spec_augment is True``."
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},
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)
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gradient_checkpointing: Optional[bool] = field(
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default=False,
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metadata={
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"help": "If True, use gradient checkpointing to save memory at the expense of slower backward pass."
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},
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)
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layerdrop: Optional[float] = field(default=0.0, metadata={"help": "The LayerDrop probability."})
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ctc_loss_reduction: Optional[str] = field(
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default="mean", metadata={"help": "The way the ctc loss should be reduced. Should be one of 'mean' or 'sum'."}
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@@ -438,7 +432,7 @@ def main():
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"hidden_dropout": model_args.hidden_dropout,
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"final_dropout": model_args.final_dropout,
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"mask_time_prob": model_args.mask_time_prob,
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"gradient_checkpointing": model_args.gradient_checkpointing,
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"gradient_checkpointing": training_args.gradient_checkpointing,
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"layerdrop": model_args.layerdrop,
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"ctc_loss_reduction": model_args.ctc_loss_reduction,
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"pad_token_id": processor.tokenizer.pad_token_id,
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