jsnfly

increase beam size

9024e1e about 2 years ago

No virus

11.1 kB

	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)