Create tts.py

tts.py

@@ -0,0 +1,62 @@
+import os
+import torch
+import sys
+import gradio as gr
+
+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}")
+
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+    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()
+
+    return gr.Audio.update(value=(hps.data.sampling_rate, hyp)), text