import argparse import re from typing import Dict import torch from datasets import Audio, Dataset, load_dataset, load_metric from transformers import AutoFeatureExtractor, AutoTokenizer, SpeechEncoderDecoderModel, pipeline from torch import nn from torch.nn import CrossEntropyLoss from transformers.models.encoder_decoder.modeling_encoder_decoder import shift_tokens_right from transformers.modeling_outputs import Seq2SeqLMOutput 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) -> str: """DO ADAPT FOR YOUR USE CASE. this function normalizes the target text.""" # From https://huggingface.co/jonatasgrosman/wav2vec2-large-xlsr-53-german. CHARS_TO_IGNORE = [",", "?", "¿", ".", "!", "¡", ";", "；", ":", '""', "%", '"', "�", "ʿ", "·", "჻", "~", "՞", "؟", "،", "।", "॥", "«", "»", "„", "“", "”", "「", "」", "‘", "’", "《", "》", "(", ")", "[", "]", "{", "}", "=", "`", "_", "+", "<", ">", "…", "–", "°", "´", "ʾ", "‹", "›", "©", "®", "—", "→", "。", "、", "﹂", "﹁", "‧", "～", "﹏", "，", "｛", "｝", "（", "）", "［", "］", "【", "】", "‥", "〽", "『", "』", "〝", "〟", "⟨", "⟩", "〜", "：", "！", "？", "♪", "؛", "/", "\\", "º", "−", "^", "ʻ", "ˆ"] chars_to_ignore_regex = f"[{re.escape(''.join(CHARS_TO_IGNORE))}]" text = re.sub(chars_to_ignore_regex, "", text.lower()) 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 # resample audio dataset = dataset.cast_column("audio", Audio(sampling_rate=sampling_rate)) # load tokenizer tokenizer = AutoTokenizer.from_pretrained(args.model_id) # load model model = Wav2VecGPT2Model.from_pretrained(args.model_id) model.config.num_beams = 4 # 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, device=args.device, feature_extractor=feature_extractor, tokenizer=tokenizer) # map function to decode audio def map_to_pred(batch): prediction = asr( batch["audio"]["array"], chunk_length_s=args.chunk_length_s, stride_length_s=args.stride_length_s ) batch["prediction"] = normalize_text(prediction["text"]) batch["target"] = normalize_text(batch["sentence"]) return batch # 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) class Wav2VecGPT2Model(SpeechEncoderDecoderModel): """ Basically the same as `SpeechEncoderDecoderModel` but position embeddings (initialized with GPT2's position embeddings) are added to encoder output """ def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) self.encoder_outputs_pos_emb = nn.Embedding(1024, self.decoder.config.hidden_size) with torch.no_grad(): self.encoder_outputs_pos_emb.weight.copy_(self.decoder.transformer.wpe.weight) self.enc_to_dec_proj_ln = nn.LayerNorm(self.decoder.config.hidden_size, eps=self.decoder.config.layer_norm_epsilon) def __getattribute__(self, name): # Fake class so it is recognized as seq2seq model. if name == '__class__': return SpeechEncoderDecoderModel return SpeechEncoderDecoderModel.__getattribute__(self, name) def forward( self, inputs=None, attention_mask=None, decoder_input_ids=None, decoder_attention_mask=None, encoder_outputs=None, past_key_values=None, decoder_inputs_embeds=None, labels=None, use_cache=None, output_attentions=None, output_hidden_states=None, input_values=None, input_features=None, return_dict=None, **kwargs, ): return_dict = return_dict if return_dict is not None else self.config.use_return_dict kwargs_encoder = {argument: value for argument, value in kwargs.items() if not argument.startswith("decoder_")} kwargs_decoder = { argument[len("decoder_") :]: value for argument, value in kwargs.items() if argument.startswith("decoder_") } if encoder_outputs is None and inputs is None: if input_values is not None and input_features is not None: raise ValueError("You cannot specify both input_values and input_features at the same time") elif input_values is not None: inputs = input_values elif input_features is not None: inputs = input_features else: raise ValueError("You have to specify either input_values or input_features") encoder_outputs = self.encoder( inputs, attention_mask=attention_mask, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, **kwargs_encoder, ) encoder_hidden_states = encoder_outputs[0] # optionally project encoder_hidden_states if ( self.encoder_output_dim != self.decoder.config.hidden_size and self.decoder.config.cross_attention_hidden_size is None ): encoder_hidden_states = self.enc_to_dec_proj(encoder_hidden_states) encoder_hidden_states += self.encoder_outputs_pos_emb( torch.arange(0, encoder_hidden_states.shape[1], device=encoder_hidden_states.device) ) encoder_hidden_states = self.enc_to_dec_proj_ln(encoder_hidden_states) # compute correct encoder attention mask if attention_mask is not None: encoder_attention_mask = self.encoder._get_feature_vector_attention_mask( encoder_hidden_states.shape[1], attention_mask ) else: encoder_attention_mask = None if (labels is not None) and (decoder_input_ids is None and decoder_inputs_embeds is None): decoder_input_ids = shift_tokens_right( labels, self.config.pad_token_id, self.config.decoder_start_token_id ) # Decode decoder_outputs = self.decoder( input_ids=decoder_input_ids, attention_mask=decoder_attention_mask, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask, inputs_embeds=decoder_inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, use_cache=use_cache, past_key_values=past_key_values, return_dict=return_dict, **kwargs_decoder, ) # Compute loss independent from decoder (as some shift the logits inside them) loss = None if labels is not None: logits = decoder_outputs.logits if return_dict else decoder_outputs[0] loss_fct = CrossEntropyLoss() loss = loss_fct(logits.reshape(-1, self.decoder.config.vocab_size), labels.view(-1)) if not return_dict: if loss is not None: return (loss,) + decoder_outputs + encoder_outputs else: return decoder_outputs + encoder_outputs return Seq2SeqLMOutput( loss=loss, logits=decoder_outputs.logits, past_key_values=decoder_outputs.past_key_values, decoder_hidden_states=decoder_outputs.hidden_states, decoder_attentions=decoder_outputs.attentions, cross_attentions=decoder_outputs.cross_attentions, encoder_last_hidden_state=encoder_outputs.last_hidden_state, encoder_hidden_states=encoder_outputs.hidden_states, encoder_attentions=encoder_outputs.attentions, ) 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( "--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.", ) args = parser.parse_args() main(args)