Kittipong/wav2vec2-th-vocal-domain
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初始化项目，由ModelHub XC社区提供模型

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Model: Kittipong/wav2vec2-th-vocal-domain
Source: Original Platform
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ModelHub XC
2026-05-27 04:48:16 +08:00
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---
license: cc-by-sa-4.0
---

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{
"_name_or_path": "facebook/wav2vec2-large-xlsr-53",
"activation_dropout": 0.0,
"apply_spec_augment": true,
"architectures": [
"Wav2Vec2ForCTC"
],
"attention_dropout": 0.1,
"bos_token_id": 1,
"codevector_dim": 768,
"contrastive_logits_temperature": 0.1,
"conv_bias": true,
"conv_dim": [
512,
512,
512,
512,
512,
512,
512
],
"conv_kernel": [
10,
3,
3,
3,
3,
2,
2
],
"conv_stride": [
5,
2,
2,
2,
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2,
2
],
"ctc_loss_reduction": "mean",
"ctc_zero_infinity": false,
"diversity_loss_weight": 0.1,
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"feat_extract_activation": "gelu",
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"model_type": "wav2vec2",
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"num_codevectors_per_group": 320,
"num_conv_pos_embedding_groups": 16,
"num_conv_pos_embeddings": 128,
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#!/usr/bin/env python3
import argparse
import re
from typing import Dict
from datasets import Audio, Dataset, load_dataset, load_metric
from transformers import AutoFeatureExtractor, pipeline
from pythainlp.tokenize import word_tokenize, syllable_tokenize
from deepcut import tokenize as deepcut_word_tokenize
from functools import partial
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"robust-speech-event/{dataset_id}_eval_results_{args.thai_tokenizer}.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"robust-speech-event/log_{dataset_id}_predictions_{args.thai_tokenizer}.txt"
target_file = f"robust-speech-event/log_{dataset_id}_targets_{args.thai_tokenizer}.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, tok_func) -> str:
"""DO ADAPT FOR YOUR USE CASE. this function normalizes the target text."""
chars_to_ignore_regex = '[,?.!\-\;\:"“%‘”<EFBFBD>—’…–]' # noqa: W605 IMPORTANT: this should correspond to the chars that were ignored during training
text = re.sub(chars_to_ignore_regex, "", text.lower())
# In addition, we can normalize the target text, e.g. removing new lines characters etc...
# note that order is important here!
token_sequences_to_ignore = ["\n\n", "\n", " ", " "]
for t in token_sequences_to_ignore:
text = " ".join(text.split(t))
#thai tokenize
text = " ".join(tok_func(text))
return text
def retokenize(text:str, tok_func) -> str:
"""tokenize and rejoin prediction outputs without cleaning"""
return " ".join(tok_func("".join(text.split())))
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 eval pipeline
asr = pipeline("automatic-speech-recognition", model=args.model_id)
#select tokenizer
if args.thai_tokenizer=='deepcut':
tok_func = deepcut_word_tokenize
elif args.thai_tokenizer=='newmm':
tok_func = word_tokenize
elif args.thai_tokenizer=='syllable':
tok_func = syllable_tokenize
else:
tok_func = lambda x: x.replace(' ','')
# map function to decode audio
def map_to_pred(batch, tok_func):
prediction = asr(
batch["audio"]["array"], chunk_length_s=args.chunk_length_s, stride_length_s=args.stride_length_s
)
batch["prediction"] = retokenize(prediction["text"], tok_func)
batch["target"] = normalize_text(batch["sentence"], tok_func)
return batch
# run inference on all examples
result = dataset.map(partial(map_to_pred, tok_func=tok_func),
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(
"--thai_tokenizer", type=str, default="newmm",
required=True, help="newmm, syllable, or deepcut; if not specified, remove all spaces (used for CER calculation)"
)
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."
)
args = parser.parse_args()
main(args)

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