demo

app.py

@@ -5,9 +5,12 @@ import librosa
 import numpy as np
 import torch
 
-from transformers import pipeline
+from transformers import SpeechT5ForTextToSpeech, SpeechT5Processor, SpeechT5HifiGan
 
-synthesiser = pipeline("text-to-speech", "techiaith/microsoft_speecht5_finetuned_bu_tts_cy_en")
+checkpoint = "microsoft/speecht5_tts"
+processor = SpeechT5Processor.from_pretrained(checkpoint)
+model = SpeechT5ForTextToSpeech.from_pretrained("techiaith/microsoft_speecht5_finetuned_bu_tts_cy_en")
+vocoder = SpeechT5HifiGan.from_pretrained("microsoft/speecht5_hifigan")
 
 speaker_embeddings = {
     "GGP": "spkemb/speaker0.npy",
@@ -21,7 +24,8 @@ def predict(text, speaker):
         return (16000, np.zeros(0).astype(np.int16))
     speaker_embedding = np.load(speaker_embeddings[speaker[:3]])
     speaker_embedding = torch.tensor(speaker_embedding).unsqueeze(0)
-    speech = synthesiser(text, forward_params={"speaker_embeddings": speaker_embedding})
+    inputs = processor(text=text, return_tensors="pt")
+    speech = model.generate_speech(inputs["input_ids"], speaker_embedding, vocoder=vocoder)
     speech = (speech.numpy() * 32767).astype(np.int16)
     return (16000, speech)