wav2vec2-large-xlsr-marathi

Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Marathi using the [Open SLR64](http://openslr.org/64/) dataset. When using this model, make sure that your speech input is sampled at 16kHz. This data contains only female voices but the model works well for male voices too. Trained on Google Colab Pro on Tesla P100 16GB GPU.<br> **WER (Word Error Rate) on the Test Set**: 12.70 % The model can be used directly without a language model as follows, given that your dataset has Marathi `actual_text` and `path_in_folder` columns: import torch, torchaudio from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor mr_test_dataset = all_data['test'] processor = Wav2Vec2Processor.from_pretrained("sumedh/wav2vec2-large-xlsr-marathi") model = Wav2Vec2ForCTC.from_pretrained("sumedh/wav2vec2-large-xlsr-marathi") resampler = torchaudio.transforms.Resample(48_000, 16_000) #first arg - input sample, second arg - output sample def speech_file_to_array_fn(batch): speech_array, sampling_rate = torchaudio.load(batch["path_in_folder"]) batch["speech"] = resampler(speech_array).squeeze().numpy() return batch mr_test_dataset = mr_test_dataset.map(speech_file_to_array_fn) inputs = processor(mr_test_dataset["speech"][:5], sampling_rate=16_000, return_tensors="pt", padding=True) with torch.no_grad(): logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits predicted_ids = torch.argmax(logits, dim=-1) print("Prediction:", processor.batch_decode(predicted_ids)) print("Reference:", mr_test_dataset["actual_text"][:5]) Evaluated on 10% of the Marathi data on Open SLR-64. import os, re, torch, torchaudio from datasets import Dataset, load_metric import pandas as pd from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor dataset_path = "./OpenSLR-64_Marathi/mr_in_female/" #TODO : include the path of the dataset extracted from http://openslr.org/64/ audio_df = pd.read_csv(os.path.join(dataset_path,'line_index.tsv'),sep='\t',header=None) audio_df.columns = ['path_in_folder','actual_text'] audio_df['path_in_folder'] = audio_df['path_in_folder'].apply(lambda x: dataset_path + x + '.wav') audio_df = audio_df.sample(frac=1, random_state=2020).reset_index(drop=True) #seed number is important for reproducibility of WER score all_data = Dataset.from_pandas(audio_df) all_data = all_data.train_test_split(test_size=0.10,seed=2020) #seed number is important for reproducibility of WER score mr_test_dataset = all_data['test'] wer = load_metric("wer") processor = Wav2Vec2Processor.from_pretrained("sumedh/wav2vec2-large-xlsr-marathi") model = Wav2Vec2ForCTC.from_pretrained("sumedh/wav2vec2-large-xlsr-marathi") model.to("cuda") chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\“]' resampler = torchaudio.transforms.Resample(48_000, 16_000) def speech_file_to_array_fn(batch): batch["actual_text"] = re.sub(chars_to_ignore_regex, '', batch["actual_text"]).lower() speech_array, sampling_rate = torchaudio.load(batch["path_in_folder"]) batch["speech"] = resampler(speech_array).squeeze().numpy() return batch mr_test_dataset = mr_test_dataset.map(speech_file_to_array_fn) def evaluate(batch): inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True) with torch.no_grad(): logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits pred_ids = torch.argmax(logits, dim=-1) batch["pred_strings"] = processor.batch_decode(pred_ids) return batch result = mr_test_dataset.map(evaluate, batched=True, batch_size=8) print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["actual_text"]))) Train-Test ratio was 90:10. The training notebook Colab link [here](https://colab.research.google.com/drive/1wX46fjExcgU5t3AsWhSPTipWg_aMDg2f?usp=sharing). weights-and-biases run summary [here](https://wandb.ai/wandb/xlsr/runs/3itdhtb8/overview?workspace=user-sumedhkhodke)