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| import gradio as gr | |
| import torch | |
| import whisper | |
| ### ———————————————————————————————————————— | |
| title="Whisper to Emotion" | |
| ### ———————————————————————————————————————— | |
| whisper_model = whisper.load_model("small") | |
| device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") | |
| def translate(audio): | |
| print(""" | |
| — | |
| Sending audio to Whisper ... | |
| — | |
| """) | |
| audio = whisper.load_audio(audio) | |
| audio = whisper.pad_or_trim(audio) | |
| mel = whisper.log_mel_spectrogram(audio).to(whisper_model.device) | |
| _, probs = whisper_model.detect_language(mel) | |
| transcript_options = whisper.DecodingOptions(task="transcribe", fp16 = False) | |
| translate_options = whisper.DecodingOptions(task="translate", fp16 = False) | |
| transcription = whisper.decode(whisper_model, mel, transcript_options) | |
| translation = whisper.decode(whisper_model, mel, translate_options) | |
| print("Language Spoken: " + transcription.language) | |
| print("Transcript: " + transcription.text) | |
| print("Translated: " + translation.text) | |
| return transcription.text | |
| gr.Markdown(""" | |
| # Emotion Detection From Speech Using Whisper | |
| """) | |
| audio_input = gr.Audio(label = 'Record Audio Input',source="microphone",type="filepath") | |
| transcript_output = gr.Textbox(label="Transcription in your the language you spoke") | |
| iface = gr.Interface(fn=translate, inputs=audio_input, outputs=transcript_output) | |
| iface.launch() |