updated with XLA hook
Browse files- run_whisper.py +139 -130
run_whisper.py
CHANGED
@@ -50,138 +50,147 @@ class DataCollatorSpeechSeq2SeqWithPadding:
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return batch
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pred_ids = pred.predictions
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label_ids = pred.label_ids
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pred_str = tokenizer.batch_decode(pred_ids, skip_special_tokens=True)
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label_str = tokenizer.batch_decode(label_ids, skip_special_tokens=True)
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#
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#
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# speech_data["train"] = load_dataset("NbAiLab/
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speech_data["
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#
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return batch
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def main():
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# Metrics
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def compute_metrics(pred):
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pred_ids = pred.predictions
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label_ids = pred.label_ids
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# replace -100 with the pad_token_id
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label_ids[label_ids == -100] = tokenizer.pad_token_id
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# we do not want to group tokens when computing the metrics
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pred_str = tokenizer.batch_decode(pred_ids, skip_special_tokens=True)
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label_str = tokenizer.batch_decode(label_ids, skip_special_tokens=True)
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wer = 100 * metric.compute(predictions=pred_str, references=label_str)
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return {"wer": wer}
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# Prepare dataset
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def prepare_dataset(batch):
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# load and resample audio data from 48 to 16kHz
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audio = batch["audio"]
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# compute log-Mel input features from input audio array
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batch["input_features"] = feature_extractor(
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audio["array"], sampling_rate=audio["sampling_rate"]).input_features[0]
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# encode target text to label ids
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batch["labels"] = tokenizer(batch["sentence"]).input_ids
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return batch
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# Whisper Trainin Script
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# Map the source and target columns
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# Whisper expects these to be "audio" and "sentence". Change if anything else in the dataset
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source = "audio"
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target = "sentence"
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# Load a sample dataset
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speech_data = DatasetDict()
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# Examples
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# speech_data["train"] = load_dataset("NbAiLab/NPSC", "16K_mp3_bokmaal", split="train", use_auth_token=True)
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# speech_data["test"] = load_dataset("NbAiLab/NPSC", "16K_mp3_bokmaal", split="test", use_auth_token=True)
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# speech_data["train"] = load_dataset("NbAiLab/LIA_speech", split="train", use_auth_token=True)
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#speech_data["test"] = load_dataset("NbAiLab/LIA_speech", split="test", use_auth_token=True)
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# The smallest dataset I found
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speech_data["train"] = load_dataset(
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"mozilla-foundation/common_voice_11_0", "nn-NO", split="train", use_auth_token=True)
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speech_data["test"] = load_dataset(
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"mozilla-foundation/common_voice_11_0", "nn-NO", split="test", use_auth_token=True)
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# Rename columns
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if "audio" not in speech_data.column_names["train"]:
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speech_data = speech_data.rename_column(source, "audio")
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if "sentence" not in speech_data.column_names["train"]:
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speech_data = speech_data.rename_column(target, "sentence")
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# Remove not needed columns - Not really sure if this is necessary
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remove_list = [i for i in speech_data.column_names["train"]
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if i not in ["audio", "sentence"]]
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speech_data = speech_data.remove_columns(remove_list)
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# Initialise
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feature_extractor = WhisperFeatureExtractor.from_pretrained(
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"openai/whisper-small")
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tokenizer = WhisperTokenizer.from_pretrained(
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"openai/whisper-small", language="Norwegian", task="transcribe")
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processor = WhisperProcessor.from_pretrained(
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"openai/whisper-small", language="Norwegian", task="transcribe")
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data_collator = DataCollatorSpeechSeq2SeqWithPadding(processor=processor)
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# Prepare data
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speech_data = speech_data.cast_column("audio", Audio(sampling_rate=16000))
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speech_data = speech_data.map(
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prepare_dataset, remove_columns=speech_data.column_names["train"], num_proc=1)
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# Metrics
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metric = evaluate.load("wer")
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# Initialise a Pretrained model
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# We need to set use_cache=False here if we want to use gradient accumulation
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model = WhisperForConditionalGeneration.from_pretrained(
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"openai/whisper-small", use_cache=False)
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# Overriding generation arguments - no tokens are forced as decoder outputs (see [`forced_decoder_ids`](https://huggingface.co/docs/transformers/main_classes/text_generation#transformers.generation_utils.GenerationMixin.generate.forced_decoder_ids)), no tokens are suppressed during generation (see [`suppress_tokens`](https://huggingface.co/docs/transformers/main_classes/text_generation#transformers.generation_utils.GenerationMixin.generate.suppress_tokens)):
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model.config.forced_decoder_ids = None
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model.config.suppress_tokens = []
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# Training arguments
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training_args = Seq2SeqTrainingArguments(
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output_dir="../whisper-test", # change to a repo name of your choice
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# Use at least 16 is reasonable. This is just for the test on Ficino
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per_device_train_batch_size=4,
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gradient_accumulation_steps=1, # increase by 2x for every 2x decrease in batch size
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learning_rate=1e-5,
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warmup_steps=500,
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max_steps=1000, # Changed from 4000
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gradient_checkpointing=True,
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fp16=True,
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group_by_length=True,
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evaluation_strategy="steps",
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per_device_eval_batch_size=8,
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predict_with_generate=True,
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generation_max_length=225,
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save_steps=500,
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eval_steps=500,
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logging_steps=25,
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report_to=["tensorboard"],
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load_best_model_at_end=True,
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metric_for_best_model="wer",
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greater_is_better=False,
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push_to_hub=True,
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)
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trainer = Seq2SeqTrainer(
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args=training_args,
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model=model,
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train_dataset=speech_data["train"],
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eval_dataset=speech_data["test"],
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data_collator=data_collator,
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compute_metrics=compute_metrics,
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tokenizer=processor.feature_extractor,
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)
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# Start training
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trainer.train()
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def _mp_fn(index):
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# For xla_spawn (TPUs)
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main()
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if __name__ == "__main__":
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main()
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