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) model_name = "indonesian-nlp/wav2vec2-indonesian-javanese-sundanese" processor = Wav2Vec2Processor.from_pretrained(model_name) model = Wav2Vec2ForCTC.from_pretrained(model_name) 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 output = gr.outputs.Textbox(label="Indonesian, Javanese or Sundanese") 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); gr.Interface(parse_transcription, inputs = input_, outputs=[output], analytics_enabled=False, show_tips=False, theme='huggingface', layout='vertical', title="Multilingual Speech Recognition for Indonesian Languages", description="Speech Recognition Live Demo for Indonesian, Javanese and Sundanese", enable_queue=True).launch( inline=False)