Final

app.py

@@ -2,10 +2,26 @@ 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):
-    client = Client("adrien-alloreview/speechbrain-lang-id-voxlingua107-ecapa")
-    result = client.predict(param_0=file(file_path), api_name="/predict")
+    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"
@@ -13,44 +29,55 @@ def detect_language(file_path):
         selected_language = "kazakh"
     return selected_language
 
-# def detect_emotion(audio):
-#     pipe = pipeline(
-#         "audio-classification",
-#         model="HowMannyMore/wav2vec2-lg-xlsr-ur-speech-emotion-recognition",
-#     )
-#     res = pipe(audio)
-#     emotion_with_max_score = res[0]["label"]
-#
-#     return emotion_with_max_score
-#
-#
-# def detect_toxic_local(text_whisper):
-#     model_name_rus = "IlyaGusev/rubertconv_toxic_clf"
-#     pipe = pipeline(
-#         "text-classification",
-#         model=model_name_rus,
-#         tokenizer=model_name_rus,
-#         framework="pt",
-#         max_length=512,
-#         truncation=True,
-#         device=0,
-#     )
-#     res = pipe([text_whisper])[0]["label"]
-#     if res == "toxic":
-#         return True
-#     if res == "neutral":
-#         return False
-#     else:
-#         return None
+
+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=detect_language,
-    inputs=gr.Audio(sources="upload", type="filepath"),
-    outputs="text",
-    title="File Upload Transcription",
-    description="Upload an audio file to determine language."
+    fn=assessment,
+    inputs=gr.Audio(sources=["upload"], type="filepath"),
+    outputs="json"
 )
+gradio_app.launch()
 
-if __name__ == "__main__":
-    gradio_app.launch()