Moreno La Quatra
Create eval.py
5770ef0
#!/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)