Detect language

app.py

@@ -1,18 +1,56 @@
-# import gradio as gr
-# from transformers import pipeline
+import gradio as gr
+from transformers import pipeline
+from gradio_client import Client, file
 
-# pipeline = pipeline(task="image-classification", model="julien-c/hotdog-not-hotdog")
 
-# def predict(input_img):
-#     predictions = pipeline(input_img)
-#     return input_img, {p["label"]: p["score"] for p in predictions} 
+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")
+    language_result = result["label"].split(": ")[1]
+    if language_result.lower() in ["russian", "belarussian", "ukrainian"]:
+        selected_language = "russian"
+    else:
+        selected_language = "kazakh"
+    return selected_language
 
-# gradio_app = gr.Interface(
-#     predict,
-#     inputs=gr.Image(label="Select hot dog candidate", sources=['upload', 'webcam'], type="pil"),
-#     outputs=[gr.Image(label="Processed Image"), gr.Label(label="Result", num_top_classes=2)],
-#     title="Hot Dog? Or Not?",
-# )
+# 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
 
-# if __name__ == "__main__":
-#     gradio_app.launch()
+
+gradio_app = gr.Interface(
+    fn=detect_language,
+    inputs=gr.Audio(source="upload", type="filepath"),
+    outputs="text",
+    title="File Upload Transcription",
+    description="Upload an audio file to determine language."
+)
+
+if __name__ == "__main__":
+    gradio_app.launch()