tanmaylaud commited on
Commit
0a1bc74
1 Parent(s): d7aa717

updated model weights

Browse files
.ipynb_checkpoints/README-checkpoint.md ADDED
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+ ---
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+ language: mr
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+ datasets:
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+ - openslr
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+ - interspeech_2021_asr
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+ metrics:
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+ - wer
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+ tags:
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+ - audio
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+ - automatic-speech-recognition
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+ - speech
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+ - xlsr-fine-tuning-week
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+ - hindi
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+ - marathi
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+ license: apache-2.0
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+ model-index:
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+ - name: XLSR Wav2Vec2 Large 53 Hindi-Marathi by Tanmay Laud
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+ results:
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+ - task:
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+ name: Speech Recognition
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+ type: automatic-speech-recognition
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+ dataset:
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+ name: OpenSLR hi, OpenSLR mr
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+ type: openslr, interspeech_2021_asr
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+ metrics:
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+ - name: Test WER
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+ type: wer
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+ value: 24.92
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+ ---
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+
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+ # Wav2Vec2-Large-XLSR-53-Hindi-Marathi
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+ Fine-tuned facebook/wav2vec2-large-xlsr-53 on Hindi and Marathi using the OpenSLR SLR64 datasets. When using this model, make sure that your speech input is sampled at 16kHz.
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+
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+ ## Usage
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+ The model can be used directly (without a language model) as follows, assuming you have a dataset with Marathi text and audio_path fields:
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+
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+ ```
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+ import torch
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+ import torchaudio
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+ import librosa
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+ from datasets import load_dataset
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+ from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
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+
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+ # test_data = #TODO: WRITE YOUR CODE TO LOAD THE TEST DATASET. For sample see the Colab link in Training Section.
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+
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+ processor = Wav2Vec2Processor.from_pretrained("tanmaylaud/wav2vec2-large-xlsr-hindi-marathi")
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+ model = Wav2Vec2ForCTC.from_pretrained("tanmaylaud/wav2vec2-large-xlsr-hindi-marathi")
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+
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+ # Preprocessing the datasets.
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+ # We need to read the audio files as arrays
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+ def speech_file_to_array_fn(batch):
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+ speech_array, sampling_rate = torchaudio.load(batch["audio_path"])
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+ batch["speech"] = librosa.resample(speech_array[0].numpy(), sampling_rate, 16_000) # sampling_rate can vary
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+ return batch
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+
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+ test_data= test_data.map(speech_file_to_array_fn)
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+ inputs = processor(test_data["speech"][:2], sampling_rate=16_000, return_tensors="pt", padding=True)
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+
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+ with torch.no_grad():
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+ logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits
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+
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+ predicted_ids = torch.argmax(logits, dim=-1)
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+
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+ print("Prediction:", processor.batch_decode(predicted_ids))
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+ print("Reference:", test_data["text"][:2])
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+ Evaluation
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+ The model can be evaluated as follows on 10% of the Marathi data on OpenSLR.
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+ ```
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+ ```
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+ import torchaudio
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+ from datasets import load_metric
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+ from transformers import Wav2Vec2Processor,Wav2Vec2ForCTC
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+ import torch
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+ import librosa
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+ import numpy as np
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+ import re
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+
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+ wer = load_metric("wer")
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+ processor = Wav2Vec2Processor.from_pretrained("tanmaylaud/wav2vec2-large-xlsr-hindi-marathi")
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+ model = Wav2Vec2ForCTC.from_pretrained("tanmaylaud/wav2vec2-large-xlsr-hindi-marathi")
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+
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+ model.to("cuda")
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+
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+ chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\“\%\‘\”\�\।]'
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+
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+ # Preprocessing the datasets.
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+ # We need to read the audio files as arrays
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+ def speech_file_to_array_fn(batch):
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+ batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"])
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+ speech_array, sampling_rate = torchaudio.load(batch["path"])
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+ batch["speech"] = speech_array[0].numpy()
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+ batch["sampling_rate"] = sampling_rate
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+ batch["target_text"] = batch["sentence"]
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+ batch["speech"] = librosa.resample(np.asarray(batch["speech"]), sampling_rate, 16_000)
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+ batch["sampling_rate"] = 16_000
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+ return batch
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+
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+ test= test.map(speech_file_to_array_fn)
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+
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+ # Preprocessing the datasets.
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+ # We need to read the audio files as arrays
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+ def evaluate(batch):
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+ inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)
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+ with torch.no_grad():
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+ logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits
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+ pred_ids = torch.argmax(logits, dim=-1)
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+ batch["pred_strings"] = processor.batch_decode(pred_ids, group_tokens=False)
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+ # we do not want to group tokens when computing the metrics
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+ return batch
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+
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+ result = test.map(evaluate, batched=True, batch_size=32)
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+ print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["text"])))
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+ ```
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+
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+ Link to eval notebook : https://colab.research.google.com/drive/1nZRTgKfxCD9cvy90wikTHkg2il3zgcqW#scrollTo=cXWFbhb0d7DT
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