import soundfile as sf import torch from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor,Wav2Vec2ProcessorWithLM import gradio as gr import scipy.signal as sps 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 ) #print(this is not done) sox_tfm.build(inputfile, outfile) def read_file(wav): sample_rate, signal = wav signal = signal.mean(-1) number_of_samples = round(len(signal) * float(16000) / sample_rate) resampled_signal = sps.resample(signal, number_of_samples) return resampled_signal def resampler(input_file_path, output_folder_path): #output_file_path = output_folder_path + input_file_path.split('/')[-1] 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(wav_file): input_values = read_file_and_process(wav_file) with torch.no_grad(): logits = model(**input_values).logits[0].cpu().numpy() print(logits) int_result = processor_with_LM.decode(logits = logits, output_word_offsets=False, beam_width=128 ) print(int_result) transcription = int_result.text.replace('','') return transcription def read_file_and_process(wav_file): filename = wav_file.split('.')[0] convert(wav_file, filename + "16k.wav") speech, _ = sf.read(filename + "16k.wav") inputs = processor(speech, sampling_rate=16_000, return_tensors="pt", padding=True) return inputs def parse(wav_file, applyLM): if applyLM: return parse_transcription_with_lm(wav_file) else: return parse_transcription(wav_file) def parse_transcription(wav_file): input_values = read_file_and_process(wav_file) with torch.no_grad(): logits = model(**input_values).logits #logits = model(input_values).logits predicted_ids = torch.argmax(logits, dim=-1) transcription = processor.decode(predicted_ids[0], skip_special_tokens=True) return transcription model_id = "Harveenchadha/vakyansh-wav2vec2-hindi-him-4200" 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") #input_ = gr.inputs.Audio(source="microphone", type="numpy") 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);