Update app.py

app.py

@@ -1,5 +1,26 @@
+import torch
+import gradio as gr
+import pytube as pt
+from transformers import pipeline
 from fastspeech2 import FastSpeech2
+
+MODEL_NAME = "openai/whisper-large-v2"
+
+device = 0 if torch.cuda.is_available() else "cpu"
+
+pipe = pipeline(
+    task="automatic-speech-recognition",
+    model=MODEL_NAME,
+    chunk_length_s=30,
+    device=device,
+)
+
+all_special_ids = pipe.tokenizer.all_special_ids
+transcribe_token_id = all_special_ids[-5]
+translate_token_id = all_special_ids[-6]
+
 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
@@ -12,7 +33,7 @@ def transcribe(microphone, state, task="transcribe"):
     text = pipe(file)["text"]
     converted_voice = convert_voice(text)
 
-    return state + "\n" + converted_voice, state + "\n" + converted_voice
+    return state + "\n" + converted_voice, state + "\n" + converted_voice, converted_voice
 
 mf_transcribe = gr.Interface(
     fn=transcribe,
@@ -31,8 +52,10 @@ mf_transcribe = gr.Interface(
     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"
+        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",
 )
+
+mf_transcribe.launch(enable_queue=True)