Update tts.py

tts.py

@@ -1,62 +1,18 @@
-import os
 import torch
-import sys
-import gradio as gr
+from transformers import SpeechT5ForTextToSpeech, SpeechT5Processor
 
-from huggingface_hub import hf_hub_download
-
-# Setup TTS env
-if "vits" not in sys.path:
-    sys.path.append("vits")
-
-from vits import commons, utils
-from vits.models import SynthesizerTrn
-
-class TextMapper(object):
-    def __init__(self, vocab_file):
-        self.symbols = [x.strip() for x in open(vocab_file, encoding="utf-8").readlines()]
-        self.SPACE_ID = self.symbols.index(" ")
-        self._symbol_to_id = {s: i for i, s in enumerate(self.symbols)}
-
-    def text_to_sequence(self, text, cleaner_names):
-        sequence = [self._symbol_to_id[symbol] for symbol in text.strip()]
-        return sequence
-
-    def get_text(self, text, hps):
-        text_norm = self.text_to_sequence(text, hps.data.text_cleaners)
-        if hps.data.add_blank:
-            text_norm = commons.intersperse(text_norm, 0)
-        return torch.LongTensor(text_norm)
-
-    def filter_oov(self, text, lang=None):
-        val_chars = self._symbol_to_id
-        return "".join(filter(lambda x: x in val_chars, text))
-
-def synthesize(text, speed):
-    if speed is None:
-        speed = 1.0
-
-    lang_code = "fao"
-
-    vocab_file = hf_hub_download(repo_id="facebook/mms-tts", filename="vocab.txt", subfolder=f"models/{lang_code}")
-    config_file = hf_hub_download(repo_id="facebook/mms-tts", filename="config.json", subfolder=f"models/{lang_code}")
-    g_pth = hf_hub_download(repo_id="facebook/mms-tts", filename="G_100000.pth", subfolder=f"models/{lang_code}")
+MODEL_ID = "microsoft/speecht5_tts"
+processor = SpeechT5Processor.from_pretrained(MODEL_ID)
+model = SpeechT5ForTextToSpeech.from_pretrained(MODEL_ID)
 
+def synthesize_speech(text):
+    inputs = processor(text, return_tensors="pt")
+    # Set device
     device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    model.to(device)
+    inputs = inputs.to(device)
 
-    hps = utils.get_hparams_from_file(config_file)
-    text_mapper = TextMapper(vocab_file)
-    net_g = SynthesizerTrn(len(text_mapper.symbols), hps.data.filter_length // 2 + 1, hps.train.segment_size // hps.data.hop_length, **hps.model)
-    net_g.to(device)
-    net_g.eval()
-    utils.load_checkpoint(g_pth, net_g, None)
-
-    text = text.lower()
-    text = text_mapper.filter_oov(text)
-    stn_tst = text_mapper.get_text(text, hps)
     with torch.no_grad():
-        x_tst = stn_tst.unsqueeze(0).to(device)
-        x_tst_lengths = torch.LongTensor([stn_tst.size(0)]).to(device)
-        hyp = net_g.infer(x_tst, x_tst_lengths, noise_scale=0.667, noise_scale_w=0.8, length_scale=1.0 / speed)[0][0, 0].cpu().float().numpy()
+        speech = model.generate(**inputs)
 
-    return gr.Audio.update(value=(hps.data.sampling_rate, hyp)), text
+    return processor.decode(speech)