| import gradio as gr | |
| from transformers import pipeline | |
| from gradio_client import Client, file | |
| language_classifier = Client("adrien-alloreview/speechbrain-lang-id-voxlingua107-ecapa") | |
| transcriber = Client("tensorlake/audio-extractors") | |
| emotion_detector = pipeline( | |
| "audio-classification", | |
| model="HowMannyMore/wav2vec2-lg-xlsr-ur-speech-emotion-recognition", | |
| ) | |
| model_name_rus = "IlyaGusev/rubertconv_toxic_clf" | |
| toxic_detector = pipeline( | |
| "text-classification", | |
| model=model_name_rus, | |
| tokenizer=model_name_rus, | |
| framework="pt", | |
| max_length=512, | |
| truncation=True, | |
| device=0, | |
| ) | |
| def detect_language(file_path): | |
| result = language_classifier.predict(param_0=file(file_path), api_name="/predict") | |
| language_result = result["label"].split(": ")[1] | |
| if language_result.lower() in ["russian", "belarussian", "ukrainian"]: | |
| selected_language = "russian" | |
| else: | |
| selected_language = "kazakh" | |
| return selected_language | |
| def request_gradio(file_path, language): | |
| try: | |
| result = transcriber.predict( | |
| audio_filepath=file(file_path), | |
| task="transcribe", | |
| batch_size=24, | |
| chunk_length_s=30, | |
| sampling_rate=16000, | |
| language=language, | |
| num_speakers=2, | |
| min_speakers=2, | |
| max_speakers=2, | |
| assisted=False, | |
| api_name="/transcribe", | |
| ) | |
| return result | |
| except Exception as e: | |
| return None | |
| def detect_emotion(audio): | |
| res = emotion_detector(audio) | |
| emotion_with_max_score = res[0]["label"] | |
| return emotion_with_max_score | |
| def detect_toxic_local(text_whisper): | |
| res = toxic_detector([text_whisper])[0]["label"] | |
| if res == "toxic": | |
| return True | |
| if res == "neutral": | |
| return False | |
| else: | |
| return None | |
| def assessment(file_path): | |
| language = detect_language(file_path) | |
| result_text = request_gradio(file_path, language) | |
| result_emotion = detect_emotion(result_text) | |
| result_toxic = detect_toxic_local(result_text) | |
| return {"emotion": result_emotion, "toxic": result_toxic} | |
| gradio_app = gr.Interface( | |
| fn=assessment, | |
| inputs=gr.Audio(sources=["upload"], type="filepath"), | |
| outputs="json" | |
| ) | |
| gradio_app.launch() | |