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| import soundfile as sf | |
| import torch | |
| from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor | |
| import argparse | |
| from glob import glob | |
| import torchaudio | |
| import subprocess | |
| import gradio as gr | |
| resampler = torchaudio.transforms.Resample(48_000, 16_000) | |
| def get_filename(wav_file): | |
| filename_local = wav_file.split('/')[-1][:-4] | |
| filename_new = '/tmp/'+filename_local+'_16.wav' | |
| subprocess.call(["sox {} -r {} -b 16 -c 1 {}".format(wav_file, str(16000), filename_new)], shell=True) | |
| return filename_new | |
| def parse_transcription(wav_file): | |
| # load pretrained model | |
| processor = Wav2Vec2Processor.from_pretrained("Harveenchadha/vakyansh-wav2vec2-hindi-him-4200") | |
| model = Wav2Vec2ForCTC.from_pretrained("Harveenchadha/vakyansh-wav2vec2-hindi-him-4200") | |
| # load audio | |
| wav_file = get_filename(wav_file.name) | |
| audio_input, sample_rate = sf.read(wav_file) | |
| #test_file = resampler(test_file[0]) | |
| # pad input values and return pt tensor | |
| input_values = processor(audio_input, sampling_rate=16_000, return_tensors="pt").input_values | |
| # INFERENCE | |
| # retrieve logits & take argmax | |
| logits = model(input_values).logits | |
| predicted_ids = torch.argmax(logits, dim=-1) | |
| # transcribe | |
| transcription = processor.decode(predicted_ids[0], skip_special_tokens=True) | |
| return transcription | |
| title = "Speech-to-Text (Hindi) using Vakyansh" | |
| description = "Upload a hindi audio clip, and let AI do the hard work of transcribing." | |
| article = "<p style='text-align: center'><a href='https://arxiv.org/abs/2104.06678'>Large-Scale Self- and Semi-Supervised Learning for Speech Translation</a></p>" | |
| gr.Interface( | |
| parse_transcription, | |
| title=title, | |
| inputs=gr.inputs.Audio(label="Record Audio File", type="file", source = "microphone"), | |
| description=description, article = article, outputs = "text").launch(inline = False) | |