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app.py

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+import gradio as gr
+import whisperx
+
+
+
+def transcribe_audio(whisper_model,filename,whisper_compute_type,whisper_device="cpu",whisper_batch_size=4,source_lang=None):
+    # model = whisperx.load_model(whisper_model, device="cuda", compute_type="float16")
+    # global WHISPER_MODEL
+    
+    WHISPER_MODEL = whisperx.load_model(whisper_model,device=whisper_device,compute_type=whisper_compute_type)
+    
+    print("Whisper model loaded")
+    
+    audio = whisperx.load_audio(filename)
+    
+    language_for_whisper = None if source_lang == "auto" else source_lang
+    
+    result = WHISPER_MODEL.transcribe(audio, batch_size=whisper_batch_size,language=language_for_whisper)
+    language = result["language"]
+    segments = result["segments"]
+    
+    # print(segments)
+
+    print(f"Detected language {language}")
+    
+
+    if source_lang == "auto":
+        detected_language = language  
+    else:
+        detected_language = source_lang
+        
+    # Formated text
+    ready_text = ""
+    
+    for segment in segments:
+        ready_text+=segment["text"] + "\n"
+
+    return ready_text
+
+def process_audio(audio_input,whisper_model,whisper_language,whisper_compute_type):
+    result= transcribe_audio(filename=audio_input, whisper_model=whisper_model,source_lang=whisper_language,whisper_compute_type=whisper_compute_type)
+    return result
+
+with gr.Blocks() as demo:
+    gr.Markdown("# Any whisper ct2 test")
+
+    with gr.Row():
+        with gr.Column(scale=1):
+            with gr.Accordion(open=False,label="Whisper settings"):
+                    whisper_model = gr.Dropdown(choices=["tiny","daswer123/whisper-medium-uz-ct2"],value="daswer123/whisper-medium-uz-ct2", allow_custom_value=True)
+                    whisper_language = gr.Dropdown(choices=["auto","uz"],value="uz", allow_custom_value=True)
+                    whisper_compute_type = gr.Dropdown(choices=["int8","float32"],value="int8")
+            audio_input = gr.Audio(label="Upload your audio",type="filepath")
+
+        with gr.Column(scale=1):
+            text_output = gr.Textbox(label="Result")
+            submit_btn = gr.Button("Start!")
+
+    submit_btn.click(fn=process_audio, inputs=[audio_input,whisper_model,whisper_language,whisper_compute_type], outputs=text_output)
+
+demo.launch()
+# result = transcribe_audio("tiny","bezbeka.mp3","float32",source_lang="uz")
+# print(result)

requirements.txt

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+gradio==4.40
+whisperx
+transformers==4.39.3