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| import whisper | |
| import gradio as gr | |
| import time | |
| import os | |
| '''model = whisper.load_model("base") | |
| print(model.device)''' | |
| def speechtotext(tmp_filename, uploaded): | |
| try: | |
| source = uploaded if uploaded is not None else tmp_filename | |
| result = os.system("whisper" + source + " --language Hindi " + " --task translate ") | |
| return f'Detected language: {Language.make(language=result["language"]).display_name()}\n\n ' \ | |
| f'You said: {result["text"]}' | |
| except: | |
| return "Unable to generate translation" | |
| gr.Interface( | |
| title="NS-AI-Labs Custom Whisper", | |
| thumbnail="https://cdn.openai.com/whisper/asr-summary-of-model-architecture-desktop.svg", | |
| css=""" | |
| .gr-prose p{text-align: center;} | |
| .gr-button {background: black;color: white} | |
| """, | |
| description="we customised whisper with some additional ASR layers , speak in any language we are here to get it " | |
| "recognised !", | |
| fn=speechtotext, | |
| inputs=[ | |
| gr.Audio(label="Record your voice on your mic", source="microphone", type="filepath"), | |
| gr.Audio(source="upload", type="filepath", label="Upload Audio")], | |
| outputs="text").launch() | |