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from datasets import load_dataset, load_metric, Audio, Dataset |
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from transformers import pipeline, AutoFeatureExtractor, AutoTokenizer, AutoConfig, AutoModelForCTC, Wav2Vec2Processor, Wav2Vec2ProcessorWithLM |
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import re |
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import torch |
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import argparse |
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from typing import Dict |
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def log_results(result: Dataset, args: Dict[str, str]): |
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""" DO NOT CHANGE. This function computes and logs the result metrics. """ |
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log_outputs = args.log_outputs |
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dataset_id = "_".join(args.dataset.split("/") + [args.config, args.split]) |
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wer = load_metric("wer") |
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cer = load_metric("cer") |
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wer_result = wer.compute(references=result["target"], predictions=result["prediction"]) |
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cer_result = cer.compute(references=result["target"], predictions=result["prediction"]) |
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result_str = ( |
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f"WER: {wer_result}\n" |
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f"CER: {cer_result}" |
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) |
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print(result_str) |
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with open(f"{dataset_id}_eval_results.txt", "w") as f: |
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f.write(result_str) |
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if log_outputs is not None: |
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pred_file = f"log_{dataset_id}_predictions.txt" |
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target_file = f"log_{dataset_id}_targets.txt" |
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with open(pred_file, "w") as p, open(target_file, "w") as t: |
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def write_to_file(batch, i): |
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p.write(f"{i}" + "\n") |
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p.write(batch["prediction"] + "\n") |
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t.write(f"{i}" + "\n") |
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t.write(batch["target"] + "\n") |
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result.map(write_to_file, with_indices=True) |
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def normalize_text(text: str, invalid_chars_regex: str, to_lower: bool) -> str: |
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""" DO ADAPT FOR YOUR USE CASE. this function normalizes the target text. """ |
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text = text.lower() if to_lower else text.upper() |
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text = re.sub(invalid_chars_regex, " ", text) |
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text = re.sub("\s+", " ", text).strip() |
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return text |
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def main(args): |
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dataset = load_dataset(args.dataset, args.config, split=args.split, use_auth_token=True) |
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if args.greedy: |
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processor = Wav2Vec2Processor.from_pretrained(args.model_id) |
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decoder = None |
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else: |
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processor = Wav2Vec2ProcessorWithLM.from_pretrained(args.model_id) |
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decoder = processor.decoder |
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feature_extractor = processor.feature_extractor |
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tokenizer = processor.tokenizer |
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dataset = dataset.cast_column("audio", Audio(sampling_rate=feature_extractor.sampling_rate)) |
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if args.device is None: |
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args.device = 0 if torch.cuda.is_available() else -1 |
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config = AutoConfig.from_pretrained(args.model_id) |
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model = AutoModelForCTC.from_pretrained(args.model_id) |
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asr = pipeline("automatic-speech-recognition", config=config, model=model, tokenizer=tokenizer, |
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feature_extractor=feature_extractor, decoder=decoder, device=args.device) |
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tokenizer = AutoTokenizer.from_pretrained(args.model_id) |
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tokens = [x for x in tokenizer.convert_ids_to_tokens(range(0, tokenizer.vocab_size))] |
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special_tokens = [ |
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tokenizer.pad_token, tokenizer.word_delimiter_token, |
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tokenizer.unk_token, tokenizer.bos_token, |
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tokenizer.eos_token, |
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] |
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non_special_tokens = [x for x in tokens if x not in special_tokens] |
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invalid_chars_regex = f"[^\s{re.escape(''.join(set(non_special_tokens)))}]" |
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normalize_to_lower = False |
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for token in non_special_tokens: |
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if token.isalpha() and token.islower(): |
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normalize_to_lower = True |
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break |
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def map_to_pred(batch, args=args, asr=asr, invalid_chars_regex=invalid_chars_regex, normalize_to_lower=normalize_to_lower): |
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prediction = asr(batch["audio"]["array"], chunk_length_s=args.chunk_length_s, stride_length_s=args.stride_length_s) |
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batch["prediction"] = prediction["text"] |
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batch["target"] = normalize_text(batch["sentence"], invalid_chars_regex, normalize_to_lower) |
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return batch |
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result = dataset.map(map_to_pred, remove_columns=dataset.column_names) |
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result = result.filter(lambda example: example["target"] != "") |
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log_results(result, args) |
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if __name__ == "__main__": |
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parser = argparse.ArgumentParser() |
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parser.add_argument( |
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"--model_id", type=str, required=True, help="Model identifier. Should be loadable with 🤗 Transformers" |
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) |
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parser.add_argument( |
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"--dataset", type=str, required=True, help="Dataset name to evaluate the `model_id`. Should be loadable with 🤗 Datasets" |
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) |
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parser.add_argument( |
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"--config", type=str, required=True, help="Config of the dataset. *E.g.* `'en'` for Common Voice" |
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) |
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parser.add_argument( |
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"--split", type=str, required=True, help="Split of the dataset. *E.g.* `'test'`" |
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) |
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parser.add_argument( |
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"--chunk_length_s", type=float, default=None, help="Chunk length in seconds. Defaults to None. For long audio files a good value would be 5.0 seconds." |
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) |
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parser.add_argument( |
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"--stride_length_s", type=float, default=None, help="Stride of the audio chunks. Defaults to None. For long audio files a good value would be 1.0 seconds." |
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) |
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parser.add_argument( |
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"--log_outputs", action='store_true', help="If defined, write outputs to log file for analysis." |
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) |
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parser.add_argument( |
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"--greedy", action='store_true', help="If defined, the LM will be ignored during inference." |
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) |
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parser.add_argument( |
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"--device", |
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type=int, |
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default=None, |
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help="The device to run the pipeline on. -1 for CPU (default), 0 for the first GPU and so on.", |
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) |
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args = parser.parse_args() |
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main(args) |
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