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): 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_with_lm(logits): result = processor_with_LM.batch_decode(logits.cpu().numpy()) text = result.text transcription = text[0] return transcription def parse_transcription(logits): predicted_ids = torch.argmax(logits, dim=-1) transcription = processor.decode(predicted_ids[0], skip_special_tokens=True) return transcription def parse(wav_file, applyLM): input_values = read_file_and_process(wav_file) with torch.no_grad(): logits = model(**input_values).logits if applyLM: return parse_transcription_with_lm(logits) else: return parse_transcription(logits) model_id = "aditii09/facebook_english_asr" processor = Wav2Vec2Processor.from_pretrained(model_id) processor_with_LM = Wav2Vec2ProcessorWithLM.from_pretrained(model_id) model = Wav2Vec2ForCTC.from_pretrained(model_id) input_ = gr.Audio(source="microphone", type="filepath") txtbox = gr.Textbox( label="Output from model will appear here:", lines=5 ) chkbox = gr.Checkbox(label="Apply LM", value=False) gr.Interface(parse, inputs = [input_, chkbox], outputs=txtbox, streaming=True, interactive=True, analytics_enabled=False, show_tips=False, enable_queue=True).launch(inline=False);