| import gradio as gr | |
| import numpy as np | |
| from transformers import WhisperProcessor, WhisperForConditionalGeneration | |
| import librosa | |
| processor = WhisperProcessor.from_pretrained("Neurai/NeuraSpeech_WhisperBase") | |
| model = WhisperForConditionalGeneration.from_pretrained("Neurai/NeuraSpeech_WhisperBase") | |
| forced_decoder_ids = processor.get_decoder_prompt_ids(language="fa", task="transcribe") | |
| def transcribe(audio): | |
| if audio is None: | |
| return "No audio input provided. Please record or upload an audio file." | |
| array, sample_rate = librosa.load(audio) | |
| array = array.astype(np.float32) | |
| sr = 16000 | |
| array = librosa.to_mono(array) | |
| array = librosa.resample(array, orig_sr=sample_rate, target_sr=sr) | |
| input_features = processor(array, sampling_rate=sr, return_tensors="pt").input_features | |
| predicted_ids = model.generate(input_features) | |
| transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True) | |
| return transcription[0] | |
| demo = gr.Interface( | |
| fn=transcribe, | |
| inputs=[gr.Audio(sources=["microphone"], type='filepath')], | |
| outputs="text", | |
| allow_flagging="never", | |
| ) | |
| if __name__ == "__main__": | |
| demo.launch() | |