--- language: mr datasets: - openslr - interspeech_2021_asr metrics: - wer tags: - audio - automatic-speech-recognition - speech - xlsr-fine-tuning-week - hindi - marathi license: apache-2.0 model-index: - name: XLSR Wav2Vec2 Large 53 Hindi-Marathi by Tanmay Laud results: - task: name: Speech Recognition type: automatic-speech-recognition dataset: name: OpenSLR hi, OpenSLR mr type: openslr, interspeech_2021_asr metrics: - name: Test WER type: wer value: 60.80 --- # Wav2Vec2-Large-XLSR-53-Hindi-Marathi ### Fine-tuned facebook/wav2vec2-large-xlsr-53 on Hindi and Marathi using the OpenSLR SLR64 datasets. Note that this data OpenSLR contains only female voices. Please keep this in mind before using the model for your task. When using this model, make sure that your speech input is sampled at 16kHz. ## Usage The model can be used directly (without a language model) as follows, assuming you have a dataset with Marathi text and audio_path fields: ``` import torch import torchaudio import librosa from datasets import load_dataset from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor # test_data = #TODO: WRITE YOUR CODE TO LOAD THE TEST DATASET. For sample see the Colab link in Training Section. processor = Wav2Vec2Processor.from_pretrained("tanmaylaud/wav2vec2-large-xlsr-hindi-marathi") model = Wav2Vec2ForCTC.from_pretrained("tanmaylaud/wav2vec2-large-xlsr-hindi-marathi") # Preprocessing the datasets. # We need to read the audio files as arrays def speech_file_to_array_fn(batch): speech_array, sampling_rate = torchaudio.load(batch["audio_path"]) batch["speech"] = librosa.resample(speech_array[0].numpy(), sampling_rate, 16_000) # sampling_rate can vary return batch test_data= test_data.map(speech_file_to_array_fn) inputs = processor(test_data["speech"][:2], 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:", test_data["text"][:2]) Evaluation The model can be evaluated as follows on 10% of the Marathi data on OpenSLR. ``` ``` import torch import torchaudio import librosa from datasets import load_dataset, load_metric from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor import re # test_data = #TODO: WRITE YOUR CODE TO LOAD THE TEST DATASET. For sample see the Colab link in Training Section. wer = load_metric("wer") processor = Wav2Vec2Processor.from_pretrained("tanmaylaud/wav2vec2-large-xlsr-hindi-marathi") model = Wav2Vec2ForCTC.from_pretrained("tanmaylaud/wav2vec2-large-xlsr-hindi-marathi") model.to("cuda") chars_to_ignore_regex = '[\\\\,\\\\?\\\\.\\\\!\\\\-\\\\;\\\\:\\\\"\\\\“\\\\%\\\\‘\\\\”\\\\�\\\\–\\\\…]' # Preprocessing the datasets. # We need to read the audio files as arrays def speech_file_to_array_fn(batch): batch["text"] = re.sub(chars_to_ignore_regex, '', batch["text"]).lower() speech_array, sampling_rate = torchaudio.load(batch["audio_path"]) batch["speech"] = librosa.resample(speech_array[0].numpy(), sampling_rate, 16_000) return batch test_data= test_data.map(speech_file_to_array_fn) # Preprocessing the datasets. # We need to read the audio files as arrays 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 = test_data.map(evaluate, batched=True, batch_size=8) print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["text"]))) ```