| import streamlit as st | |
| import requests | |
| import pyaudio | |
| import wave | |
| import os | |
| import tempfile | |
| API_URL = "https://api-inference.huggingface.co/models/openai/whisper-large-v3-turbo" | |
| headers = {"Authorization": f"Bearer {st.secrets['hf_token']}"} | |
| def query(file): | |
| data = file.read() | |
| response = requests.post(API_URL, headers=headers, data=data) | |
| return response.json() | |
| def record_audio(duration=5, sample_rate=44100, chunk=1024, channels=1): | |
| p = pyaudio.PyAudio() | |
| stream = p.open(format=pyaudio.paInt16, | |
| channels=channels, | |
| rate=sample_rate, | |
| input=True, | |
| frames_per_buffer=chunk) | |
| st.info(f"Recording for {duration} seconds...") | |
| frames = [] | |
| for i in range(0, int(sample_rate / chunk * duration)): | |
| data = stream.read(chunk) | |
| frames.append(data) | |
| st.info("Recording finished.") | |
| stream.stop_stream() | |
| stream.close() | |
| p.terminate() | |
| return frames, sample_rate | |
| st.title("Speech Recognition with Whisper") | |
| option = st.radio("Choose input method:", ('Upload File', 'Record from Microphone')) | |
| if option == 'Upload File': | |
| uploaded_file = st.file_uploader("Choose an audio file", type=['wav', 'mp3', 'flac']) | |
| if uploaded_file is not None: | |
| st.audio(uploaded_file, format='audio/wav') | |
| file_to_transcribe = uploaded_file | |
| else: | |
| duration = st.slider("Recording duration (seconds)", 1, 30, 5) | |
| if st.button('Start Recording'): | |
| frames, sample_rate = record_audio(duration=duration) | |
| with tempfile.NamedTemporaryFile(delete=False, suffix='.wav') as tmpfile: | |
| wf = wave.open(tmpfile.name, 'wb') | |
| wf.setnchannels(1) | |
| wf.setsampwidth(pyaudio.PyAudio().get_sample_size(pyaudio.paInt16)) | |
| wf.setframerate(sample_rate) | |
| wf.writeframes(b''.join(frames)) | |
| wf.close() | |
| st.audio(tmpfile.name, format='audio/wav') | |
| file_to_transcribe = open(tmpfile.name, 'rb') | |
| if 'file_to_transcribe' in locals(): | |
| if st.button('Transcribe'): | |
| with st.spinner('Transcribing...'): | |
| result = query(file_to_transcribe) | |
| if 'text' in result: | |
| st.success("Transcription completed!") | |
| st.write("Transcribed text:") | |
| st.write(result['text']) | |
| else: | |
| st.error("An error occurred during transcription.") | |
| st.write("Error details:") | |
| st.write(result) | |
| if option == 'Record from Microphone': | |
| os.unlink(file_to_transcribe.name) | |
| st.markdown("---") | |
| st.write("Note: This app uses the Whisper API from Hugging Face.") |