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| import gradio as gr | |
| import torch | |
| import whisper | |
| import warnings | |
| warnings.filterwarnings('ignore') | |
| from transformers import pipeline | |
| import os | |
| MODEL_NAME = "openai/whisper-small" | |
| BATCH_SIZE = 8 | |
| device = 0 if torch.cuda.is_available() else "cpu" | |
| pipe = pipeline( | |
| task="automatic-speech-recognition", | |
| model=MODEL_NAME, | |
| chunk_length_s=30, | |
| device=device) | |
| emotion_classifier = pipeline("text-classification",model='MilaNLProc/xlm-emo-t', return_all_scores=True) | |
| def transcribe(microphone, file_upload, task): | |
| output = "" | |
| if (microphone is not None) and (file_upload is not None): | |
| warn_output = ( | |
| "WARNING: You've uploaded an audio file and used the microphone. " | |
| "The recorded file from the microphone will be used and the uploaded audio will be discarded.\n" | |
| ) | |
| elif (microphone is None) and (file_upload is None): | |
| raise gr.Error("You have to either use the microphone or upload an audio file") | |
| file = microphone if microphone is not None else file_upload | |
| text = pipe(file, batch_size=BATCH_SIZE, generate_kwargs={"task": task})["text"] | |
| return output + text | |
| def translate_and_classify(audio): | |
| text_result = transcribe(audio, None, "transcribe") | |
| emotion = emotion_classifier(text_result) | |
| detected_emotion = {} | |
| for emotion in emotion[0]: | |
| detected_emotion[emotion["label"]] = emotion["score"] | |
| return text_result, detected_emotion | |
| with gr.Blocks() as demo: | |
| gr.Markdown( | |
| """ # Emotion Detection from Speech | |
| ##### Detection of anger, sadness, joy, fear in speech using OpenAI Whisper and XLM-RoBERTa | |
| """) | |
| with gr.Column(): | |
| with gr.Tab("Record Audio"): | |
| # The 'source' argument is no longer supported, use 'sources' instead | |
| audio_input_r = gr.Audio(label = 'Record Audio Input',sources=["microphone"],type="filepath") | |
| transcribe_audio_r = gr.Button('Transcribe') | |
| with gr.Tab("Upload Audio as File"): | |
| # The 'source' argument is no longer supported, use 'sources' instead | |
| audio_input_u = gr.Audio(label = 'Upload Audio',sources=["upload"],type="filepath") | |
| transcribe_audio_u = gr.Button('Transcribe') | |
| with gr.Row(): | |
| transcript_output = gr.Textbox(label="Transcription in the language of speech/audio", lines = 3) | |
| emotion_output = gr.Label(label = "Detected Emotion") | |
| transcribe_audio_r.click(translate_and_classify, inputs = audio_input_r, outputs = [transcript_output,emotion_output]) | |
| transcribe_audio_u.click(translate_and_classify, inputs = audio_input_u, outputs = [transcript_output,emotion_output]) | |
| demo.launch(share=True) |