Update app.py

app.py

@@ -11,7 +11,7 @@ device = "cuda:0" if torch.cuda.is_available() else "cpu"
 asr_pipe = pipeline("automatic-speech-recognition", model="openai/whisper-large-v2", device=device)
 
 # load text-to-speech checkpoint and speaker embeddings
-model_id = "Sandiago21/speecht5_finetuned_voxpopuli_it"  # update with your model id
+model_id = "Sandiago21/speecht5_finetuned_facebook_voxpopuli_french"  # update with your model id
 # pipe = pipeline("automatic-speech-recognition", model=model_id)
 model = SpeechT5ForTextToSpeech.from_pretrained(model_id)
 vocoder = SpeechT5HifiGan.from_pretrained("microsoft/speecht5_hifigan")
@@ -47,7 +47,7 @@ def synthesize_speech(text):
     return gr.Audio.update(value=(16000, speech.cpu().numpy()))
 
 def translate(audio):
-    outputs = asr_pipe(audio, max_new_tokens=256, generate_kwargs={"task": "transcribe", "language": "italian"})
+    outputs = asr_pipe(audio, max_new_tokens=256, generate_kwargs={"task": "transcribe", "language": "french"})
     return outputs["text"]