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| import gradio as gr | |
| import requests | |
| import wave | |
| import pyaudio | |
| import soundfile as sf | |
| import os | |
| # API URL and headers | |
| API_URL = "https://api-inference.huggingface.co/models/openai/whisper-large-v3" | |
| headers = {"Authorization": f"Bearer {HF_TOKEN}"} | |
| # Audio configuration | |
| FORMAT = pyaudio.paInt16 | |
| CHANNELS = 1 | |
| RATE = 16000 # Whisper models expect 16kHz | |
| CHUNK = 1024 | |
| class AudioRecorder: | |
| def __init__(self): | |
| self.is_recording = False | |
| self.frames = [] | |
| self.audio = pyaudio.PyAudio() | |
| def start_recording(self): | |
| """Starts audio recording.""" | |
| self.is_recording = True | |
| self.frames = [] | |
| self.stream = self.audio.open( | |
| format=FORMAT, | |
| channels=CHANNELS, | |
| rate=RATE, | |
| input=True, | |
| frames_per_buffer=CHUNK | |
| ) | |
| def record_chunk(self): | |
| """Records a chunk of audio.""" | |
| if self.is_recording: | |
| data = self.stream.read(CHUNK, exception_on_overflow=False) | |
| self.frames.append(data) | |
| def stop_recording(self): | |
| """Stops the audio recording.""" | |
| self.is_recording = False | |
| self.stream.stop_stream() | |
| self.stream.close() | |
| def save_audio(self, filename="output.wav"): | |
| """Saves the recorded audio to a WAV file.""" | |
| with wave.open(filename, 'wb') as wf: | |
| wf.setnchannels(CHANNELS) | |
| wf.setsampwidth(self.audio.get_sample_size(FORMAT)) | |
| wf.setframerate(RATE) | |
| wf.writeframes(b''.join(self.frames)) | |
| # Convert to FLAC | |
| flac_filename = "output.flac" | |
| data, samplerate = sf.read(filename) | |
| sf.write(flac_filename, data, samplerate, format='FLAC') | |
| return flac_filename | |
| def close(self): | |
| self.audio.terminate() | |
| recorder = AudioRecorder() | |
| def start_recording(): | |
| recorder.start_recording() | |
| return "Recording started." | |
| def record_audio(): | |
| recorder.record_chunk() | |
| return "Recording in progress..." | |
| def stop_and_transcribe(): | |
| try: | |
| recorder.stop_recording() | |
| flac_file = recorder.save_audio() | |
| with open(flac_file, "rb") as f: | |
| response = requests.post( | |
| API_URL, | |
| headers=headers, | |
| data=f.read() | |
| ) | |
| if response.status_code == 200: | |
| result = response.json() | |
| return result.get("text", "No transcription available.") | |
| else: | |
| return f"API error: {response.status_code}" | |
| except Exception as e: | |
| return f"Error: {str(e)}" | |
| finally: | |
| if os.path.exists("output.wav"): | |
| os.remove("output.wav") | |
| if os.path.exists("output.flac"): | |
| os.remove("output.flac") | |
| # Define Gradio interface | |
| def build_interface(): | |
| with gr.Blocks() as demo: | |
| gr.Markdown("# Speech-to-Text Transcription with Whisper") | |
| with gr.Row(): | |
| start_button = gr.Button("Start Recording") | |
| stop_button = gr.Button("Stop and Transcribe") | |
| transcription_output = gr.Textbox(label="Transcription") | |
| start_button.click(start_recording, outputs=None) | |
| stop_button.click(stop_and_transcribe, outputs=transcription_output) | |
| return demo | |
| if __name__ == "__main__": | |
| interface = build_interface() | |
| interface.launch() | |