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| import soundfile as sf | |
| import torch | |
| from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor,Wav2Vec2ProcessorWithLM | |
| import gradio as gr | |
| import sox | |
| import subprocess | |
| def read_file_and_process(wav_file, processor): | |
| filename = wav_file.split('.')[0] | |
| filename_16k = filename + "16k.wav" | |
| resampler(wav_file, filename_16k) | |
| speech, _ = sf.read(filename_16k) | |
| inputs = processor(speech, sampling_rate=16_000, return_tensors="pt", padding=True) | |
| return inputs | |
| def resampler(input_file_path, output_file_path): | |
| command = ( | |
| f"ffmpeg -hide_banner -loglevel panic -i {input_file_path} -ar 16000 -ac 1 -bits_per_raw_sample 16 -vn " | |
| f"{output_file_path}" | |
| ) | |
| subprocess.call(command, shell=True) | |
| def parse_transcription(logits,processor): | |
| predicted_ids = torch.argmax(logits, dim=-1) | |
| transcription = processor.decode(predicted_ids[0], skip_special_tokens=True) | |
| return transcription | |
| def parse(wav_file, language): | |
| if language == 'Hindi': | |
| processor = Wav2Vec2Processor.from_pretrained("Harveenchadha/vakyansh-wav2vec2-hindi-him-4200") | |
| model = Wav2Vec2ForCTC.from_pretrained("Harveenchadha/vakyansh-wav2vec2-hindi-him-4200") | |
| elif language == 'Odia': | |
| processor = Wav2Vec2Processor.from_pretrained("Harveenchadha/vakyansh-wav2vec2-odia-orm-100") | |
| model = Wav2Vec2ForCTC.from_pretrained("Harveenchadha/vakyansh-wav2vec2-odia-orm-100") | |
| elif language == 'Assamese': | |
| processor = Wav2Vec2Processor.from_pretrained("Harveenchadha/vakyansh-wav2vec2-assamese-asm-8") | |
| model = Wav2Vec2ForCTC.from_pretrained("Harveenchadha/vakyansh-wav2vec2-assamese-asm-8") | |
| elif language == 'Sanskrit': | |
| processor = Wav2Vec2Processor.from_pretrained("Harveenchadha/vakyansh-wav2vec2-sanskrit-sam-60") | |
| model = Wav2Vec2ForCTC.from_pretrained("Harveenchadha/vakyansh-wav2vec2-sanskrit-sam-60") | |
| elif language == 'Punjabi': | |
| processor = Wav2Vec2Processor.from_pretrained("Harveenchadha/vakyansh-wav2vec2-punjabi-pam-10") | |
| model = Wav2Vec2ForCTC.from_pretrained("Harveenchadha/vakyansh-wav2vec2-punjabi-pam-10") | |
| elif language == 'Urdu': | |
| processor = Wav2Vec2Processor.from_pretrained("Harveenchadha/vakyansh-wav2vec2-urdu-urm-60") | |
| model = Wav2Vec2ForCTC.from_pretrained("Harveenchadha/vakyansh-wav2vec2-urdu-urm-60") | |
| elif language == 'Rajasthani': | |
| processor = Wav2Vec2Processor.from_pretrained("Harveenchadha/vakyansh-wav2vec2-rajasthani-raj-45") | |
| model = Wav2Vec2ForCTC.from_pretrained("Harveenchadha/vakyansh-wav2vec2-rajasthani-raj-45") | |
| elif language == 'Marathi': | |
| processor = Wav2Vec2Processor.from_pretrained("Harveenchadha/vakyansh-wav2vec2-marathi-mrm-100") | |
| model = Wav2Vec2ForCTC.from_pretrained("Harveenchadha/vakyansh-wav2vec2-marathi-mrm-100") | |
| elif language == 'Malayalam': | |
| processor = Wav2Vec2Processor.from_pretrained("Harveenchadha/vakyansh-wav2vec2-malayalam-mlm-8") | |
| model = Wav2Vec2ForCTC.from_pretrained("Harveenchadha/vakyansh-wav2vec2-malayalam-mlm-8") | |
| elif language == 'Maithili': | |
| processor = Wav2Vec2Processor.from_pretrained("Harveenchadha/vakyansh-wav2vec2-maithili-maim-50") | |
| model = Wav2Vec2ForCTC.from_pretrained("Harveenchadha/vakyansh-wav2vec2-maithili-maim-50") | |
| elif language == 'Dogri': | |
| processor = Wav2Vec2Processor.from_pretrained("Harveenchadha/vakyansh-wav2vec2-dogri-doi-55") | |
| model = Wav2Vec2ForCTC.from_pretrained("Harveenchadha/vakyansh-wav2vec2-dogri-doi-55") | |
| elif language == 'Bhojpuri': | |
| processor = Wav2Vec2Processor.from_pretrained("Harveenchadha/vakyansh-wav2vec2-bhojpuri-bhom-60") | |
| model = Wav2Vec2ForCTC.from_pretrained("Harveenchadha/vakyansh-wav2vec2-bhojpuri-bhom-60") | |
| elif language == 'Tamil': | |
| processor = Wav2Vec2Processor.from_pretrained("Harveenchadha/vakyansh-wav2vec2-tamil-tam-250") | |
| model = Wav2Vec2ForCTC.from_pretrained("Harveenchadha/vakyansh-wav2vec2-tamil-tam-250") | |
| elif language == 'Telugu': | |
| processor = Wav2Vec2Processor.from_pretrained("Harveenchadha/vakyansh-wav2vec2-telugu-tem-100") | |
| model = Wav2Vec2ForCTC.from_pretrained("Harveenchadha/vakyansh-wav2vec2-telugu-tem-100") | |
| elif language == 'Nepali': | |
| processor = Wav2Vec2Processor.from_pretrained("Harveenchadha/vakyansh-wav2vec2-nepali-nem-130") | |
| model = Wav2Vec2ForCTC.from_pretrained("Harveenchadha/vakyansh-wav2vec2-nepali-nem-130") | |
| elif language == 'Kannada': | |
| processor = Wav2Vec2Processor.from_pretrained("Harveenchadha/vakyansh-wav2vec2-kannada-knm-560") | |
| model = Wav2Vec2ForCTC.from_pretrained("Harveenchadha/vakyansh-wav2vec2-kannada-knm-560") | |
| elif language == 'Gujarati': | |
| processor = Wav2Vec2Processor.from_pretrained("Harveenchadha/vakyansh-wav2vec2-gujarati-gnm-100") | |
| model = Wav2Vec2ForCTC.from_pretrained("Harveenchadha/vakyansh-wav2vec2-gujarati-gnm-100") | |
| elif language == 'Bengali': | |
| processor = Wav2Vec2Processor.from_pretrained("Harveenchadha/vakyansh-wav2vec2-bengali-bnm-200") | |
| model = Wav2Vec2ForCTC.from_pretrained("Harveenchadha/vakyansh-wav2vec2-bengali-bnm-200") | |
| elif language == 'English': | |
| processor = Wav2Vec2Processor.from_pretrained("Harveenchadha/vakyansh-wav2vec2-indian-english-enm-700") | |
| model = Wav2Vec2ForCTC.from_pretrained("Harveenchadha/vakyansh-wav2vec2-indian-english-enm-700") | |
| input_values = read_file_and_process(wav_file, processor) | |
| with torch.no_grad(): | |
| logits = model(**input_values).logits | |
| return parse_transcription(logits, processor) | |
| options = ['Hindi','Odia','Assamese','Sanskrit','Punjabi','Urdu','Rajasthani','Marathi','Malayalam','Maithili','Dogri','Bhojpuri','Tamil','Telugu','Nepali','Kannada','Gujarati','Bengali','English'] | |
| language = gr.Dropdown(options,label="Select language") | |
| input_ = gr.Audio(source="upload", type="filepath") | |
| txtbox = gr.Textbox( | |
| label="Output from model will appear here:", | |
| lines=5 | |
| ) | |
| gr.Interface(parse, inputs = [input_,language ], outputs=txtbox, | |
| streaming=True, interactive=True, | |
| analytics_enabled=False, show_tips=False, enable_queue=True).launch(inline=False); |