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)