import soundfile as sf import librosa import torch from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor import gradio as gr import os api_token = os.getenv("API_TOKEN") model_name = "indonesian-nlp/wav2vec2-indonesian-javanese-sundanese" processor = Wav2Vec2Processor.from_pretrained(model_name, use_auth_token=api_token) model = Wav2Vec2ForCTC.from_pretrained(model_name, use_auth_token=api_token) device = "cuda" if torch.cuda.is_available() else "cpu" model = model.to(device) def convert(inputfile, outfile): target_sr = 16000 data, sample_rate = librosa.load(inputfile) data = librosa.resample(data, orig_sr=sample_rate, target_sr=target_sr) sf.write(outfile, data, target_sr) 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 input_values = input_values.to(device) 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="The transcript") input_ = gr.inputs.Audio(source="microphone", type="file") gr.Interface(parse_transcription, inputs=input_, outputs=[output], analytics_enabled=False, title="Multilingual Speech Recognition for Indonesian Languages", description="Automatic Speech Recognition Live Demo for Indonesian, Javanese and Sundanese Language", article="This demo was built for the project " "Multilingual Speech Recognition for Indonesian Languages. " "It uses the indonesian-nlp/wav2vec2-indonesian-javanese-sundanese model " "which was fine-tuned on Indonesian Common Voice, Javanese and Sundanese OpenSLR speech datasets." ).launch( inline=False, server_name="0.0.0.0", show_tips=False, enable_queue=True)