Update app.py

app.py

@@ -3,39 +3,35 @@ import numpy as np
 import torch
 from datasets import load_dataset
 
-from transformers import SpeechT5ForTextToSpeech, SpeechT5HifiGan, SpeechT5Processor, pipeline
+from transformers import (SpeechT5ForTextToSpeech, 
+                          SpeechT5HifiGan, SpeechT5Processor, 
+                          VitsModel, VitsTokenizer
+                          pipeline)
 
 
-device = "cuda:0" if torch.cuda.is_available() else "cpu"
-
-# load speech translation checkpoint
+device = "cpu"
+checkpoint = "Matthijs/mms-tts-fra"
 asr_pipe = pipeline("automatic-speech-recognition", model="openai/whisper-base", device=device)
-
-# load text-to-speech checkpoint and speaker embeddings
-processor = SpeechT5Processor.from_pretrained("imvladikon/speech_t5_voxpopuli_nl")
-
-model = SpeechT5ForTextToSpeech.from_pretrained("imvladikon/speech_t5_voxpopuli_nl").to(device)
-vocoder = SpeechT5HifiGan.from_pretrained("imvladikon/speech_t5_voxpopuli_nl").to(device)
-
-embeddings_dataset = load_dataset("Matthijs/cmu-arctic-xvectors", split="validation")
-speaker_embeddings = torch.tensor(embeddings_dataset[7306]["xvector"]).unsqueeze(0)
+model = VitsModel.from_pretrained(checkpoint)
+tokenizer = VitsTokenizer.from_pretrained(checkpoint)
 
 
 def translate(audio):
-    outputs = asr_pipe(audio, max_new_tokens=256, generate_kwargs={"task": "translate", "language": "dutch"})
+    outputs = asr_pipe(audio, max_new_tokens=256, generate_kwargs={"task": "transcribe", "language": "french"})
     return outputs["text"]
 
 
 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)
+    inputs = tokenizer(text=text, return_tensors="pt")
+    speech_output = model(inputs["input_ids"].to(device))
+    speech = speech_output.audio[0]
     return speech.cpu()
 
 
 def speech_to_speech_translation(audio):
     translated_text = translate(audio)
     synthesised_speech = synthesise(translated_text)
-    synthesised_speech = (synthesised_speech.numpy() * 32767).astype(np.int16)
+    synthesised_speech = (synthesised_speech.detach().numpy() * 32767).astype(np.int16)
     return 16000, synthesised_speech