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import weakref
import torch
import numpy as np
from tqdm import tqdm
from transformers import Wav2Vec2ProcessorWithLM, Wav2Vec2Processor
from transformers import AutoModelForCTC, Wav2Vec2Processor, Wav2Vec2ForCTC
from datasets import load_dataset, load_metric, Audio
import fire
from aspram.utils import clean_characters, prepare_dataset
# import sentencepiece as spm
# repo_name = "20220414-210228_lm"
# repo_name = "./20220414-210228_lm_spm_bpe"
def exec(
*,
repo_name: str,
dataset: str = "yerevann/common_voice_9_0",
cuda: bool = True,
batch_size: int = 8,
beam_width: int = 1,
j: int = 1,
sample_rate: int = 16_000,
alpha: float = None,
beta: float = None,
unk_score_offset: float = None,
lm_score_boundary: bool = None,
beam_prune_logp: float = None,
token_min_logp: float = None,
output_file : str = None,
):
# repo_name = "20220428-094209--72000_lm"
print(f'loading model {repo_name}')
model = Wav2Vec2ForCTC.from_pretrained(repo_name)
print('done')
if cuda:
print('CUDA mode')
model.cuda()
if repo_name.endswith('_lm'):
processor = Wav2Vec2ProcessorWithLM.from_pretrained(repo_name, sample_rate=sample_rate)
with_lm = True
else:
processor = Wav2Vec2Processor.from_pretrained(repo_name, sample_rate=sample_rate)
with_lm = False
common_voice_test = load_dataset(
dataset,
"hy-AM",
split="test",
use_auth_token=True,
)
common_voice_test = common_voice_test.map(clean_characters)
common_voice_test = common_voice_test.cast_column(
"audio", Audio(sampling_rate=sample_rate)
)
common_voice_test = common_voice_test.map(
prepare_dataset,
remove_columns=common_voice_test.column_names,
fn_kwargs=dict(processor=processor)
)
# wer_metric = load()...
# for batch in batched_dataset:
# input_dict = processer(batch)
# logits = model(input...)
# wer_metric.update(true, pred)
# wer_metric.compute
# def exec_cer_wer(batch_size: int = 8, **kwargs):
def predict(batch):
# print(1)
input_dict = processor(
batch["input_values"],
return_tensors="pt",
padding=True,
sampling_rate=sample_rate
)
# print(2)
with torch.no_grad():
x = input_dict.input_values
if cuda:
x = x.cuda()
logits = model(x).logits
# print(3)
if with_lm:
# print(beam_size)
# sp = spm.SentencePieceProcessor()
# sp.load('head_mes_lower_bpe.model')
pred = processor.batch_decode(
logits.cpu().numpy(),
beam_width=beam_width,
alpha=alpha,
beta=beta,
unk_score_offset=unk_score_offset,
lm_score_boundary=lm_score_boundary,
num_processes=j,
beam_prune_logp=beam_prune_logp, #-1000,
token_min_logp=token_min_logp,
# sp=sp,
).text
else:
pred = processor.batch_decode(
logits.cpu().numpy().argmax(-1),
)
# print(pred)
# print(pred)
return {
'sentence': pred
}
with_predictions = common_voice_test.map(predict, batched=True, batch_size=batch_size)
def detokenize(sample):
if '▁' in sample['sentence']:
print("------ ", sample)
sample['sentence'] = sample['sentence'].replace(' ', '').replace('▁', ' ')
print("------ ", sample)
return sample
with_predictions = with_predictions.map(detokenize)
common_voice_test_transcription = load_dataset(
dataset,
"hy-AM",
split="test",
use_auth_token=True,
)
with_predictions = with_predictions.map(clean_characters, fn_kwargs=dict(lower=True, only_mesropatar=True))
common_voice_test_transcription = common_voice_test_transcription.map(clean_characters, fn_kwargs=dict(lower=True, only_mesropatar=True))
predictions = with_predictions['sentence']
references = common_voice_test_transcription['sentence']
wer_metric = load_metric("wer")
cer_metric = load_metric("cer")
for ref, pred in zip(references, predictions):
print(f' REF:\t{ref}')
print(f'PRED:\t{pred}')
print('\n')
wer = wer_metric.compute(predictions=predictions, references=references)
cer = cer_metric.compute(predictions=predictions, references=references)
print("wer: ", wer)
print("cer: ", cer)
df = common_voice_test_transcription.to_pandas()['sentence']
df = df.to_frame()
df["predictions"] = with_predictions.to_pandas()['sentence']
# df.insert(2, "predictions", with_predictions['sentence'], True)
if output_file is not None:
df.to_csv(output_file)
# exec_cer_wer(beam_width=beam_width, batch_size=batch_size)
# for pruning_score in {-10, -100, -2000}:
# for alpha in {1, 0.5, 1.5}:
# for beta in {1, 0.5, 1.5}:
# for beam_size in {0, 2, 4, 6}:
# print("Configuration:")
# print("alpha {alpha} beta {beta}, beam_width {beam_size}, pruning_score {pruning_score}".format(alpha = alpha, beta = beta, beam_size = beam_size, pruning_score = pruning_score))
# exec_cer_wer(alpha, beta, 2**beam_size, pruning_score, batch_size=batch_size)
# print('\n\n')
if __name__ == "__main__":
fire.Fire(exec) |