app.py

import gradio as gr
import spaces
import os, torch, io
import json

os.system("python -m unidic download")
import httpx

# print("Make sure you've downloaded unidic (python -m unidic download) for this WebUI to work.")
from melo.api import TTS
import tempfile
import wave
from pydub import AudioSegment
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
    TextIteratorStreamer,
    BitsAndBytesConfig,
)

quantization_config = BitsAndBytesConfig(
    load_in_4bit=True, bnb_4bit_compute_dtype=torch.float16
)

model = AutoModelForCausalLM.from_pretrained(
    "NousResearch/Hermes-2-Pro-Llama-3-8B",
    quantization_config=quantization_config,
    token=token,
)
tok = AutoTokenizer.from_pretrained("NousResearch/Hermes-2-Pro-Llama-3-8B", token=token)
terminators = [tok.eos_token_id, tok.convert_tokens_to_ids("<|eot_id|>")]


def fetch_text(url):
    prefix_url = "https://r.jina.ai/"
    url = prefix_url + url
    response = httpx.get(url, timeout=60.0)
    return response.text


@spaces.GPU
def synthesize(article_url, progress=gr.Progress()):
    text = fetch_text(article_url)
    template = """
        {
            "conversation": [
                {"speaker": "", "text": ""},
                {"speaker": "", "text": ""}
            ]
        }
        """

    chat = [
        {
            "role": "user",
            "content": f"{text} \n Convert the text as Elaborate Conversation between two people as Podcast.\nfollowing this template \n {template}",
        }
    ]
    messages = tok.apply_chat_template(chat, tokenize=False, add_generation_prompt=True)
    model_inputs = tok([messages], return_tensors="pt").to(device)

    text = model.generate(
        model_inputs,
        max_new_tokens=1024,
        do_sample=True,
        temperature=0.9,
        eos_token_id=terminators,
    )

    speed = 1.0
    device = "cuda" if torch.cuda.is_available() else "cpu"
    models = {
        "EN": TTS(language="EN", device=device),
    }
    speakers = ["EN-Default", "EN-US"]

    combined_audio = AudioSegment.empty()
    conversation = json.loads(text)
    for i, turn in enumerate(conversation["conversation"]):
        bio = io.BytesIO()
        text = turn["text"]
        speaker = speakers[i % 2]
        speaker_id = models["EN"].hps.data.spk2id[speaker]
        models["EN"].tts_to_file(
            text, speaker_id, bio, speed=speed, pbar=progress.tqdm, format="wav"
        )
        bio.seek(0)
        audio_segment = AudioSegment.from_file(bio, format="wav")
        combined_audio += audio_segment

    final_audio_path = "final.mp3"
    combined_audio.export(final_audio_path, format="mp3")
    return final_audio_path


with gr.Blocks() as demo:
    gr.Markdown("# Not Ready to USE")
    gr.Markdown("# Turn Any Article into Podcast")
    gr.Markdown("## Easily convert articles from URLs into listenable audio Podcast.")
    with gr.Group():
        text = gr.Textbox(label="Article Link")
    btn = gr.Button("Podcasitfy", variant="primary")
    aud = gr.Audio(interactive=False)
    btn.click(synthesize, inputs=[text], outputs=[aud])

demo.queue(api_open=True, default_concurrency_limit=10).launch(show_api=True)