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#!/usr/bin/env python3
import torch
import re
import argparse
from datasets import load_dataset, load_metric, Audio, Dataset
from transformers import pipeline, AutoFeatureExtractor, Wav2Vec2ProcessorWithLM, Wav2Vec2Processor
from transformers import Wav2Vec2ForCTC, AutoModelForCTC, AutoProcessor
from typing import Dict
def log_results(result: Dataset, args: Dict[str, str]):
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}"
)
with open(f"{dataset_id}_eval_results.txt", "w") as f:
f.write(result_str)
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 remove_special_characters(batch):
chars_to_remove_regex = '[\,\?\.\!\-\;\:\"\“\%\‘\”\�\'\&\/\d\_\\\]'
batch["sentence"] = re.sub(chars_to_remove_regex, '', batch["sentence"]).lower()
batch["sentence"] = re.sub('\u200c', '', batch["sentence"])
batch["sentence"] = re.sub('[a-z]', '', batch["sentence"])
return batch
def main(args):
# load dataset
dataset = load_dataset(args.dataset, args.config)
train_testvalid = dataset[args.split].train_test_split(test_size=0.25)
dataset_train = train_testvalid["train"]
dataset_test = train_testvalid["test"]
# load processor
feature_extractor = AutoFeatureExtractor.from_pretrained(args.model_id)
sampling_rate = feature_extractor.sampling_rate
print(sampling_rate)
dataset = dataset_test.map(remove_special_characters)
# resample audio
dataset = dataset.cast_column("audio", Audio(sampling_rate=sampling_rate))
processor = Wav2Vec2ProcessorWithLM.from_pretrained(args.model_id)
model = Wav2Vec2ForCTC.from_pretrained(args.model_id)
# processor = AutoProcessor.from_pretrained(args.model_id)
# model = AutoModelForCTC.from_pretrained(args.model_id)
model.to("cuda")
# load eval pipeline
# asr = pipeline("automatic-speech-recognition", model=args.model_id)
# # 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"] = prediction["text"]
# batch["target"] = batch["sentence"]
# return batch
def evaluate(batch):
inputs = processor(batch["audio"]["array"], return_tensors="pt",sampling_rate=sampling_rate, padding=True)
with torch.no_grad():
logits = model(inputs.input_values.to("cuda")).logits
# pred_ids = torch.argmax(logits, dim=-1)
# batch["prediction"] = processor.batch_decode(pred_ids)
batch["prediction"] = processor.batch_decode(logits.cpu().numpy()).text
batch["target"] =batch["sentence"]
return batch
result = dataset.map(evaluate, remove_columns=dataset.column_names)
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 None. For long audio files a good value would be 5.0 seconds."
)
parser.add_argument(
"--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."
)
parser.add_argument(
"--log_outputs", action='store_true', help="If defined, write outputs to log file for analysis."
)
args = parser.parse_args()
main(args)
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