Update app.py

app.py

@@ -14,10 +14,10 @@ asr_pipe = pipeline("automatic-speech-recognition", model="openai/whisper-base",
 # load text-to-speech checkpoint and speaker embeddings
 processor = SpeechT5Processor.from_pretrained("microsoft/speecht5_tts")
 
-model = SpeechT5ForTextToSpeech.from_pretrained("microsoft/speecht5_tts").to(device)
-vocoder = SpeechT5HifiGan.from_pretrained("microsoft/speecht5_hifigan").to(device)
-#model = VitsModel.from_pretrained("facebook/mms-tts-fra")
-#tokenizer = VitsTokenizer.from_pretrained("facebook/mms-tts-fra")
+#model = SpeechT5ForTextToSpeech.from_pretrained("microsoft/speecht5_tts").to(device)
+#vocoder = SpeechT5HifiGan.from_pretrained("microsoft/speecht5_hifigan").to(device)
+model = VitsModel.from_pretrained("facebook/mms-tts-fra")
+tokenizer = VitsTokenizer.from_pretrained("facebook/mms-tts-fra")
 
 embeddings_dataset = load_dataset("Matthijs/cmu-arctic-xvectors", split="validation")
 speaker_embeddings = torch.tensor(embeddings_dataset[7306]["xvector"]).unsqueeze(0)
@@ -29,12 +29,12 @@ def translate(audio):
 
 
 def synthesise(text):
-    inputs = processor(text=text, return_tensors="pt")
-    speech = model.generate_speech(inputs["input_ids"].to(device), speaker_embeddings.to(device), vocoder=vocoder)
-    output = speech
-    #inputs = tokenizer(text, return_tensors="pt")
-    #speech = model(inputs["input_ids"])
-    #output = speech["waveform"].detach()
+    #inputs = processor(text=text, return_tensors="pt")
+    #speech = model.generate_speech(inputs["input_ids"].to(device), speaker_embeddings.to(device), vocoder=vocoder)
+    #output = speech
+    inputs = tokenizer(text, return_tensors="pt")
+    speech = model(inputs["input_ids"])
+    output = torch.squeeze(speech["waveform"].detach())
     print(output)
     print(output.size())
     print(torch.min(output))