Create app.py

app.py

@@ -0,0 +1,56 @@
+import io
+import os
+import torch
+import torchaudio
+from TTS.api import TTS
+from TTS.tts.configs.xtts_config import XttsConfig
+from TTS.tts.models.xtts import Xtts
+from TTS.utils.generic_utils import get_user_data_dir
+
+import gradio as gr
+from scipy.io.wavfile import write
+
+os.environ["COQUI_TOS_AGREED"] = "1"
+tts = TTS("tts_models/multilingual/multi-dataset/xtts_v1")
+
+model_path = os.path.join(get_user_data_dir("tts"), "tts_models--multilingual--multi-dataset--xtts_v1")
+config = XttsConfig()
+config.load_json(os.path.join(model_path, "config.json"))
+model = Xtts.init_from_config(config)
+model.load_checkpoint(
+    config,
+    checkpoint_path=os.path.join(model_path, "model.pth"),
+    vocab_path=os.path.join(model_path, "vocab.json"),
+    eval=True,
+    use_deepspeed=True
+)
+model.cuda()
+
+def stream_audio(synthesis_text):
+    gpt_cond_latent, _, speaker_embedding = model.get_conditioning_latents(audio_path="female.wav")
+    wav_chunks = []
+
+    chunks = model.inference_stream(
+        synthesis_text,
+        "en",
+        gpt_cond_latent,
+        speaker_embedding,
+        stream_chunk_size=10,
+        overlap_wav_len=512)
+
+    for i, chunk in enumerate(chunks):
+        print(f"Received chunk {i} of audio length {chunk.shape[-1]}")
+        bytes_wav = bytes()
+        byte_io = io.BytesIO(bytes_wav)
+        write(byte_io, 24000, chunk.detach().cpu().numpy().squeeze())
+        result_bytes = byte_io.read()
+        yield result_bytes
+
+demo = gr.Interface(
+    fn=stream_audio,
+    inputs=gr.Textbox(),
+    outputs=gr.Audio(autoplay=True, streaming=True),
+)
+
+if __name__ == "__main__":
+    demo.queue().launch(debug=True)