--- language: th datasets: - common_voice tags: - audio - automatic-speech-recognition - speech - xlsr-fine-tuning - robust-speech-event license: cc-by-sa-4.0 --- # `wav2vec2-large-xlsr-53-th` Finetuning `wav2vec2-large-xlsr-53` on Thai [Common Voice 7.0](https://commonvoice.mozilla.org/en/datasets) [Read more on our blog](https://medium.com/airesearch-in-th/airesearch-in-th-3c1019a99cd) We finetune [wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) based on [Fine-tuning Wav2Vec2 for English ASR](https://colab.research.google.com/github/patrickvonplaten/notebooks/blob/master/Fine_tuning_Wav2Vec2_for_English_ASR.ipynb) using Thai examples of [Common Voice Corpus 7.0](https://commonvoice.mozilla.org/en/datasets). The notebooks and scripts can be found in [vistec-ai/wav2vec2-large-xlsr-53-th](https://github.com/vistec-ai/wav2vec2-large-xlsr-53-th). The pretrained model and processor can be found at [airesearch/wav2vec2-large-xlsr-53-th](https://huggingface.co/airesearch/wav2vec2-large-xlsr-53-th). ## 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](https://github.com/tann9949). We then deduplicate and split as described in [ekapolc/Thai_commonvoice_split](https://github.com/ekapolc/Thai_commonvoice_split) in order to 1) avoid data leakage due to random splits after cleaning in [Common Voice Corpus 7.0](https://commonvoice.mozilla.org/en/datasets) 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](https://github.com/PyThaiNLP/pythainlp) 2.3.1 and [deepcut](https://github.com/rkcosmos/deepcut), and CER. We also measure performance when spell correction using [TNC](http://www.arts.chula.ac.th/ling/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](https://github.com/vistec-AI/commonvoice-th) | 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 [cc-by-sa 4.0](https://github.com/vistec-AI/wav2vec2-large-xlsr-53-th/blob/main/LICENSE) ## Ackowledgements * model training and validation notebooks/scripts [@cstorm125](https://github.com/cstorm125/) * dataset cleaning scripts [@tann9949](https://github.com/tann9949) * dataset splits [@ekapolc](https://github.com/ekapolc/) and [@14mss](https://github.com/14mss) * running the training [@mrpeerat](https://github.com/mrpeerat) * spell correction [@wannaphong](https://github.com/wannaphong)