| import soundfile as sf | |
| import torch | |
| from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor | |
| import gradio as gr | |
| import sox | |
| def convert(inputfile, outfile): | |
| sox_tfm = sox.Transformer() | |
| sox_tfm.set_output_format( | |
| file_type="wav", channels=1, encoding="signed-integer", rate=16000, bits=16 | |
| ) | |
| sox_tfm.build(inputfile, outfile) | |
| def parse_transcription(wav_file): | |
| filename = wav_file.name.split('.')[0] | |
| convert(wav_file.name, filename + "16k.wav") | |
| speech, _ = sf.read(filename + "16k.wav") | |
| input_values = processor(speech, sampling_rate=16_000, return_tensors="pt").input_values | |
| logits = model(input_values).logits | |
| predicted_ids = torch.argmax(logits, dim=-1) | |
| transcription = processor.decode(predicted_ids[0], skip_special_tokens=True) | |
| return transcription | |
| processor = Wav2Vec2Processor.from_pretrained("Harveenchadha/vakyansh-wav2vec2-hindi-him-4200") | |
| model = Wav2Vec2ForCTC.from_pretrained("Harveenchadha/vakyansh-wav2vec2-hindi-him-4200") | |
| processor = Wav2Vec2Processor.from_pretrained("Harveenchadha/vakyansh-wav2vec2-hindi-him-4200") | |
| model = Wav2Vec2ForCTC.from_pretrained("Harveenchadha/vakyansh-wav2vec2-hindi-him-4200") | |
| input_ = gr.inputs.Audio(source="microphone", type="file") | |
| gr.Interface(parse_transcription, inputs = input_, outputs="text", | |
| analytics_enabled=False, show_tips=False, enable_queue=True).launch(inline=False); |