#!/usr/bin/env python3 import argparse import re from typing import Dict from sklearn import feature_extraction import torch from src.data.normalization import normalize_string from datasets import Audio, Dataset, load_dataset, load_metric from transformers import ( AutoFeatureExtractor, pipeline, AutoTokenizer, Wav2Vec2Processor, Wav2Vec2ProcessorWithLM, Wav2Vec2ForCTC, AutoConfig, ) def log_results(result: Dataset, args: Dict[str, str]): """DO NOT CHANGE. This function computes and logs the result metrics.""" log_outputs = args.log_outputs dataset_id = "_".join(args.dataset.split("/") + [args.config, args.split]) # load metric wer = load_metric("wer") cer = load_metric("cer") # compute metrics wer_result = wer.compute( references=result["target"], predictions=result["prediction"] ) cer_result = cer.compute( references=result["target"], predictions=result["prediction"] ) # print & log results result_str = f"WER: {wer_result}\n" f"CER: {cer_result}" print(result_str) with open(f"{dataset_id}_eval_results.txt", "w") as f: f.write(result_str) # log all results in text file. Possibly interesting for analysis if log_outputs is not None: pred_file = f"log_{dataset_id}_predictions.txt" target_file = f"log_{dataset_id}_targets.txt" with open(pred_file, "w") as p, open(target_file, "w") as t: # mapping function to write output def write_to_file(batch, i): p.write(f"{i}" + "\n") p.write(batch["prediction"] + "\n") t.write(f"{i}" + "\n") t.write(batch["target"] + "\n") result.map(write_to_file, with_indices=True) def normalize_text(text: str, invalid_chars_regex: str, to_lower: bool) -> str: """DO ADAPT FOR YOUR USE CASE. this function normalizes the target text.""" text = normalize_string(text) text = text.lower() if to_lower else text.upper() text = re.sub(invalid_chars_regex, " ", text) text = re.sub("\s+", " ", text).strip() return text def main(args): # load dataset dataset = load_dataset( args.dataset, args.config, split=args.split, use_auth_token=True ) # for testing: only process the first two examples as a test # dataset = dataset.select(range(10)) # load processor # feature_extractor = AutoFeatureExtractor.from_pretrained(args.model_id) # sampling_rate = feature_extractor.sampling_rate if args.ctcdecode: processor = Wav2Vec2ProcessorWithLM.from_pretrained(args.model_id) decoder = processor.decoder else: processor = Wav2Vec2Processor.from_pretrained(args.model_id) decoder = None feature_extractor = processor.feature_extractor tokenizer = processor.tokenizer sampling_rate = feature_extractor.sampling_rate config = AutoConfig.from_pretrained(args.model_id) model = Wav2Vec2ForCTC.from_pretrained(args.model_id) # resample audio dataset = dataset.cast_column("audio", Audio(sampling_rate=sampling_rate)) # load eval pipeline if args.device is None: args.device = 0 if torch.cuda.is_available() else -1 asr = pipeline( "automatic-speech-recognition", model=model, config=config, feature_extractor=feature_extractor, decoder=decoder, tokenizer=tokenizer, device=args.device, ) # build normalizer config tokenizer = AutoTokenizer.from_pretrained(args.model_id) tokens = [ x for x in tokenizer.convert_ids_to_tokens(range(0, tokenizer.vocab_size)) ] special_tokens = [ tokenizer.pad_token, tokenizer.word_delimiter_token, tokenizer.unk_token, tokenizer.bos_token, tokenizer.eos_token, ] non_special_tokens = [x for x in tokens if x not in special_tokens] invalid_chars_regex = f"[^\s{re.escape(''.join(set(non_special_tokens)))}]" normalize_to_lower = False for token in non_special_tokens: if token.isalpha() and token.islower(): normalize_to_lower = True break # map function to decode audio def map_to_pred( batch, args=args, asr=asr, invalid_chars_regex=invalid_chars_regex, normalize_to_lower=normalize_to_lower, ): prediction = asr( batch["audio"]["array"], chunk_length_s=args.chunk_length_s, stride_length_s=args.stride_length_s, #decoder_kwargs={"beam_width": args.beam_width}, ) batch["prediction"] = prediction["text"] batch["target"] = normalize_text( batch["sentence"], invalid_chars_regex, normalize_to_lower ) return batch def map_and_decode(batch): inputs = processor( batch["audio"]["array"], sampling_rate=batch["audio"]["sampling_rate"], return_tensors="pt", ) with torch.no_grad(): logits = model(**inputs).logits transcription = processor.batch_decode(logits.numpy()).text batch["prediction"] = transcription batch["target"] = normalize_text( batch["sentence"], invalid_chars_regex, normalize_to_lower ) return batch # transcription = .lower() # run inference on all examples result = dataset.map(map_to_pred, remove_columns=dataset.column_names) # compute and log_results # do not change function below log_results(result, args) if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument( "--model_id", type=str, required=True, help="Model identifier. Should be loadable with 🤗 Transformers", ) parser.add_argument( "--dataset", type=str, required=True, help="Dataset name to evaluate the `model_id`. Should be loadable with 🤗 Datasets", ) parser.add_argument( "--config", type=str, required=True, help="Config of the dataset. *E.g.* `'en'` for Common Voice", ) parser.add_argument( "--split", type=str, required=True, help="Split of the dataset. *E.g.* `'test'`" ) parser.add_argument( "--chunk_length_s", type=float, default=None, help="Chunk length in seconds. Defaults to 5 seconds.", ) parser.add_argument( "--stride_length_s", type=float, default=None, help="Stride of the audio chunks. Defaults to 1 second.", ) parser.add_argument( "--log_outputs", action="store_true", help="If defined, write outputs to log file for analysis.", ) parser.add_argument( "--ctcdecode", action="store_true", help="Apply the ctc decoder to the output (only if present in the model card).", ) parser.add_argument( "--device", type=int, default=None, help="The device to run the pipeline on. -1 for CPU (default), 0 for the first GPU and so on.", ) parser.add_argument( "--beam_width", type=int, default=1, help="Beam width used by the pyctc decoder.", ) args = parser.parse_args() main(args)