language: th
datasets:
- common_voice
tags:
- audio
- automatic-speech-recognition
- hf-asr-leaderboard
- robust-speech-event
- speech
- xlsr-fine-tuning
license: cc-by-sa-4.0
model-index:
- name: XLS-R-53 - Thai
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 7
type: mozilla-foundation/common_voice_7_0
args: th
metrics:
- name: Test WER
type: wer
value: 0.9524
- name: Test SER
type: ser
value: 1.2346
- name: Test CER
type: cer
value: 0.1623
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Dev Data
type: speech-recognition-community-v2/dev_data
args: sv
metrics:
- name: Test WER
type: wer
value: null
- name: Test SER
type: ser
value: null
- name: Test CER
type: cer
value: null
wav2vec2-large-xlsr-53-th
Finetuning wav2vec2-large-xlsr-53 on Thai Common Voice 7.0
We finetune wav2vec2-large-xlsr-53 based on Fine-tuning Wav2Vec2 for English ASR using Thai examples of Common Voice Corpus 7.0. The notebooks and scripts can be found in vistec-ai/wav2vec2-large-xlsr-53-th. The pretrained model and processor can be found at airesearch/wav2vec2-large-xlsr-53-th.
robust-speech-event
Add syllable_tokenize, word_tokenize (PyThaiNLP) and deepcut tokenizers to eval.py from robust-speech-event
> python eval.py --model_id ./ --dataset mozilla-foundation/common_voice_7_0 --config th --split test --log_outputs --thai_tokenizer newmm/syllable/deepcut/cer
Eval results on Common Voice 7 "test":
| WER PyThaiNLP 2.3.1 | WER deepcut | SER | CER | |
|---|---|---|---|---|
| Only Tokenization | 0.9524% | 2.5316% | 1.2346% | 0.1623% |
| Cleaning rules and Tokenization | TBD | TBD | TBD | TBD |
Usage
#load pretrained processor and model
processor = Wav2Vec2Processor.from_pretrained("airesearch/wav2vec2-large-xlsr-53-th")
model = Wav2Vec2ForCTC.from_pretrained("airesearch/wav2vec2-large-xlsr-53-th")
#function to resample to 16_000
def speech_file_to_array_fn(batch,
text_col="sentence",
fname_col="path",
resampling_to=16000):
speech_array, sampling_rate = torchaudio.load(batch[fname_col])
resampler=torchaudio.transforms.Resample(sampling_rate, resampling_to)
batch["speech"] = resampler(speech_array)[0].numpy()
batch["sampling_rate"] = resampling_to
batch["target_text"] = batch[text_col]
return batch
#get 2 examples as sample input
test_dataset = test_dataset.map(speech_file_to_array_fn)
inputs = processor(test_dataset["speech"][:2], sampling_rate=16_000, return_tensors="pt", padding=True)
#infer
with torch.no_grad():
logits = model(inputs.input_values,).logits
predicted_ids = torch.argmax(logits, dim=-1)
print("Prediction:", processor.batch_decode(predicted_ids))
print("Reference:", test_dataset["sentence"][:2])
>> Prediction: ['และ เขา ก็ สัมผัส ดีบุก', 'คุณ สามารถ รับทราบ เมื่อ ข้อความ นี้ ถูก อ่าน แล้ว']
>> Reference: ['และเขาก็สัมผัสดีบุก', 'คุณสามารถรับทราบเมื่อข้อความนี้ถูกอ่านแล้ว']
Datasets
Common Voice Corpus 7.0](https://commonvoice.mozilla.org/en/datasets) contains 133 validated hours of Thai (255 total hours) at 5GB. We pre-tokenize with pythainlp.tokenize.word_tokenize. We preprocess the dataset using cleaning rules described in notebooks/cv-preprocess.ipynb by @tann9949. We then deduplicate and split as described in ekapolc/Thai_commonvoice_split in order to 1) avoid data leakage due to random splits after cleaning in Common Voice Corpus 7.0 and 2) preserve the majority of the data for the training set. The dataset loading script is scripts/th_common_voice_70.py. You can use this scripts together with train_cleand.tsv, validation_cleaned.tsv and test_cleaned.tsv to have the same splits as we do. The resulting dataset is as follows:
DatasetDict({
train: Dataset({
features: ['path', 'sentence'],
num_rows: 86586
})
test: Dataset({
features: ['path', 'sentence'],
num_rows: 2502
})
validation: Dataset({
features: ['path', 'sentence'],
num_rows: 3027
})
})
Training
We fintuned using the following configuration on a single V100 GPU and chose the checkpoint with the lowest validation loss. The finetuning script is scripts/wav2vec2_finetune.py
# create model
model = Wav2Vec2ForCTC.from_pretrained(
"facebook/wav2vec2-large-xlsr-53",
attention_dropout=0.1,
hidden_dropout=0.1,
feat_proj_dropout=0.0,
mask_time_prob=0.05,
layerdrop=0.1,
gradient_checkpointing=True,
ctc_loss_reduction="mean",
pad_token_id=processor.tokenizer.pad_token_id,
vocab_size=len(processor.tokenizer)
)
model.freeze_feature_extractor()
training_args = TrainingArguments(
output_dir="../data/wav2vec2-large-xlsr-53-thai",
group_by_length=True,
per_device_train_batch_size=32,
gradient_accumulation_steps=1,
per_device_eval_batch_size=16,
metric_for_best_model='wer',
evaluation_strategy="steps",
eval_steps=1000,
logging_strategy="steps",
logging_steps=1000,
save_strategy="steps",
save_steps=1000,
num_train_epochs=100,
fp16=True,
learning_rate=1e-4,
warmup_steps=1000,
save_total_limit=3,
report_to="tensorboard"
)
Evaluation
We benchmark on the test set using WER with words tokenized by PyThaiNLP 2.3.1 and deepcut, and CER. We also measure performance when spell correction using TNC ngrams is applied. Evaluation codes can be found in notebooks/wav2vec2_finetuning_tutorial.ipynb. Benchmark is performed on test-unique split.
| WER PyThaiNLP 2.3.1 | WER deepcut | CER | |
|---|---|---|---|
| Kaldi from scratch | 23.04 | 7.57 | |
| Ours without spell correction | 13.634024 | 8.152052 | 2.813019 |
| Ours with spell correction | 17.996397 | 14.167975 | 5.225761 |
| Google Web Speech API※ | 13.711234 | 10.860058 | 7.357340 |
| Microsoft Bing Speech API※ | 12.578819 | 9.620991 | 5.016620 |
| Amazon Transcribe※ | 21.86334 | 14.487553 | 7.077562 |
| NECTEC AI for Thai Partii API※ | 20.105887 | 15.515631 | 9.551027 |
※ APIs are not finetuned with Common Voice 7.0 data
LICENSE
Ackowledgements
- model training and validation notebooks/scripts @cstorm125
- dataset cleaning scripts @tann9949
- dataset splits @ekapolc and @14mss
- running the training @mrpeerat
- spell correction @wannaphong