Create app.py

app.py

@@ -0,0 +1,38 @@
+from fastspeech2 import FastSpeech2
+voice_conversion_model = FastSpeech2.from_pretrained("path/to/pretrained/voice_conversion_model")
+def convert_voice(text):
+    converted_voice = voice_conversion_model(text)
+    return converted_voice
+
+def transcribe(microphone, state, task="transcribe"):
+    file = microphone
+
+    pipe.model.config.forced_decoder_ids = [[2, transcribe_token_id if task=="transcribe" else translate_token_id]]
+
+    text = pipe(file)["text"]
+    converted_voice = convert_voice(text)
+
+    return state + "\n" + converted_voice, state + "\n" + converted_voice
+
+mf_transcribe = gr.Interface(
+    fn=transcribe,
+    inputs=[
+        gr.Audio(source="microphone", type="filepath", optional=True),
+        gr.State(value="")
+    ],
+    outputs=[
+        gr.Textbox(lines=15),
+        gr.State(),
+        gr.Audio(type="auto")  # Add this line to include the converted voice as an output
+    ],
+    layout="horizontal",
+    theme="huggingface",
+    title="Whisper Large V2: Transcribe Audio and Voice Conversion",
+    live=True,
+    description=(
+        "Transcribe long-form microphone or audio inputs and convert the voice with the click of a button! Demo uses the"
+        f" checkpoint ~[{MODEL_NAME}](https://huggingface.co/{MODEL_NAME})~ and 🤗 Transformers to transcribe audio files"
+        " of arbitrary length and FastSpeech2 for voice conversion."
+    ),
+    allow_flagging="never",
+)