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| from transformers import pipeline | |
| import gradio as gr | |
| from pytube import YouTube | |
| import os | |
| pipe = pipeline(model="dussen/whisper-small-nl-hc") | |
| print(pipe) | |
| def download_audio(url, output_path='downloads'): | |
| try: | |
| # Create a YouTube object | |
| yt = YouTube(url) | |
| # Get the audio stream with the highest quality | |
| audio_stream = yt.streams.filter(only_audio=True, file_extension='mp4').first() | |
| audio_stream.download(output_path) | |
| print(f"Downloaded audio to {output_path}") | |
| # If a video.mp4 file already exists, delete it | |
| if os.path.exists(f"{output_path}/video.mp4"): | |
| os.remove(f"{output_path}/video.mp4") | |
| print("Downloading video...") | |
| # Change the name of the file to video.mp4 | |
| default_filename = audio_stream.default_filename | |
| mp4_path = f"{output_path}/{default_filename}" | |
| mp3_path = f"{output_path}/video.mp3" | |
| os.rename(mp4_path, mp3_path) | |
| print("Downloaded video") | |
| print("Transcribing audio...") | |
| print("Type of audio: ", type(mp3_path)) | |
| # Use the model to transcribe the audio | |
| text = pipe(mp3_path)["text"] | |
| print(f"Transcribed audio: {text}") | |
| # Delete the audio file | |
| os.remove(mp3_path) | |
| return text | |
| except Exception as e: | |
| print(f"Error: {e}") | |
| def audio_to_text(audio): | |
| text = pipe(audio)["text"] | |
| print(text) | |
| return text | |
| def radio_to_text(radio_url): | |
| # A radio livestream | |
| stream_url = radio_url | |
| r = requests.get(stream_url, stream=True) | |
| with open('stream.mp3', 'wb') as f: | |
| try: | |
| for block in r.iter_content(1024): | |
| f.write(block) | |
| except KeyboardInterrupt: | |
| pass | |
| text = pipe("stream.mp3")["text"] | |
| print(text) | |
| return text | |
| iface_video_url = gr.Interface( | |
| fn=download_audio, | |
| inputs="text", | |
| outputs="text", | |
| title="Whisper Small Dutch - Use a YouTube URL", | |
| description="Demo for dutch speech recognition using a fine-tuned Whisper small model.", | |
| ) | |
| iface_audio = gr.Interface( | |
| fn=audio_to_text, | |
| inputs=gr.Audio(sources=["microphone"], type="filepath"), | |
| outputs="text", | |
| title="Whisper Small Dutch - Use your microphone", | |
| description="Realtime demo for dutch speech recognition using a fine-tuned Whisper small model.", | |
| ) | |
| iface_radio = gr.Interface( | |
| fn=radio_to_text, | |
| inputs="text", | |
| outputs="text", | |
| title="Whisper Small Dutch - Use a radio URL", | |
| description="Demo for dutch speech recognition using a fine-tuned Whisper small model.", | |
| ) | |
| app = gr.TabbedInterface([iface_audio, iface_video_url, iface_radio], ["Audio to text", "Video to text", "Radio to text"]) | |
| if __name__ == "__main__": | |
| app.launch() | |