Create app.py

app.py

@@ -0,0 +1,44 @@
+import gradio as gr
+import torch
+import numpy as np
+import soundfile as sf
+import librosa
+from transformers import pipeline
+
+pipe = pipeline(
+    "automatic-speech-recognition",
+    model="antony66/whisper-large-v3-russian",
+    torch_dtype=torch.float16,
+    device=0 if torch.cuda.is_available() else -1
+)
+
+def transcribe(audio_data):
+    print(f"Received audio data: {audio_data}") 
+
+    if audio_data is None:
+        return "Ошибка: не получены аудиоданные"
+
+    wav_file = "temp_audio.wav"
+
+    if isinstance(audio_data, tuple):
+        audio_array, sample_rate = audio_data
+        sf.write(wav_file, audio_array, sample_rate)
+    elif isinstance(audio_data, str):
+        audio_array, sample_rate = librosa.load(audio_data, sr=16000)
+        sf.write(wav_file, audio_array, sample_rate)
+    else:
+        return "Ошибка: неизвестный формат аудиоданных"
+
+    result = pipe(wav_file)
+    return result["text"]
+
+with gr.Blocks() as app:
+    gr.Markdown("## Распознавание речи с Whisper")
+
+    audio_data = gr.Audio(type="filepath")
+    text_output = gr.Textbox(label="Распознанный текст")
+
+    btn = gr.Button("Распознать")
+    btn.click(transcribe, inputs=audio_data, outputs=text_output)
+
+app.launch(debug=True)