import spaces
import tempfile
import gradio as gr
import os
from whisperspeech.pipeline import Pipeline
import torch
import soundfile as sf
import numpy as np
import torch.nn.functional as F
from whisperspeech.languages import LANGUAGES
from whisperspeech.pipeline import Pipeline

title = """#🙋🏻‍♂️ Welcome to🌟Tonic's🌬️💬📝WhisperSpeech
You can use this ZeroGPU Space to test out the current model [🌬️💬📝collabora/whisperspeech](https://huggingface.co/collabora/whisperspeech). 🌬️💬📝collabora/whisperspeech is An Open Source text-to-speech system built by inverting Whisper. Previously known as spear-tts-pytorch. It's like Stable Diffusion but for speech – both powerful and easily customizable.
You can also use 🌬️💬📝WhisperSpeech by cloning this space. 🧬🔬🔍 Simply click here: <a style="display:inline-block" href="https://huggingface.co/spaces/Tonic/laion-whisper?duplicate=true"><img src="https://img.shields.io/badge/-Duplicate%20Space-blue?labelColor=white&style=flat&logo=data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABAAAAAQCAYAAAAf8/9hAAAAAXNSR0IArs4c6QAAAP5JREFUOE+lk7FqAkEURY+ltunEgFXS2sZGIbXfEPdLlnxJyDdYB62sbbUKpLbVNhyYFzbrrA74YJlh9r079973psed0cvUD4A+4HoCjsA85X0Dfn/RBLBgBDxnQPfAEJgBY+A9gALA4tcbamSzS4xq4FOQAJgCDwV2CPKV8tZAJcAjMMkUe1vX+U+SMhfAJEHasQIWmXNN3abzDwHUrgcRGmYcgKe0bxrblHEB4E/pndMazNpSZGcsZdBlYJcEL9Afo75molJyM2FxmPgmgPqlWNLGfwZGG6UiyEvLzHYDmoPkDDiNm9JR9uboiONcBXrpY1qmgs21x1QwyZcpvxt9NS09PlsPAAAAAElFTkSuQmCC&logoWidth=14" alt="Duplicate Space"></a></h3> 
Join us : 🌟TeamTonic🌟 is always making cool demos! Join our active builder's🛠️community 👻  [![Join us on Discord](https://img.shields.io/discord/1109943800132010065?label=Discord&logo=discord&style=flat-square)](https://discord.gg/GWpVpekp) On 🤗Huggingface: [TeamTonic](https://huggingface.co/TeamTonic) & [MultiTransformer](https://huggingface.co/MultiTransformer) On 🌐Github: [Polytonic](https://github.com/tonic-ai) & contribute to 🌟 [Poly](https://github.com/tonic-ai/poly) 🤗Big thanks to Yuvi Sharma and all the folks at huggingface for the community grant 🤗
"""

@spaces.GPU
def whisper_speech_demo(text, lang, speaker_audio=None, mix_lang=None, mix_text=None):
    pipe = Pipeline()

    speaker_url = None
    if speaker_audio is not None:
        speaker_url = speaker_audio.name

    if mix_lang and mix_text:
        mixed_langs = lang.split(',') + mix_lang.split(',')
        mixed_texts = [text] + mix_text.split(',')
        stoks = pipe.t2s.generate(mixed_texts, lang=mixed_langs)
        audio_data = pipe.generate(stoks, speaker_url, lang=mixed_langs[0])
    else:
        audio_data = pipe.generate(text, speaker_url, lang)

    with tempfile.NamedTemporaryFile(delete=False, suffix='.wav') as tmp_file:
        tmp_file_name = tmp_file.name
        audio_np = audio_data.numpy()  # Convert tensor to numpy array
        sf.write(tmp_file_name, audio_np, 22050)  # Assuming a sample rate of 22050 Hz

    return tmp_file_name

with gr.Blocks() as demo:
    gr.Markdown(title)
    with gr.Row():
        text_input = gr.Textbox(label="Enter text")
        lang_input = gr.Dropdown(choices=list(LANGUAGES.keys()), label="Language")
        speaker_input = gr.Audio(label="Upload or Record Speaker Audio (optional)", sources=["upload", "microphone"], type="filepath")
    with gr.Row():
        mix_lang_input = gr.CheckboxGroup(choices=list(LANGUAGES.keys()), label="Mixed Languages (optional)")
        mix_text_input = gr.Textbox(label="Mixed Texts (optional, for mixed languages)", placeholder="e.g., Hello, Cześć")
    with gr.Row():
        submit_button = gr.Button("Generate Speech")
    output_audio = gr.Audio(label="🌬️💬📝WhisperSpeech")

    submit_button.click(
        whisper_speech_demo,
        inputs=[text_input, lang_input, speaker_input, mix_lang_input, mix_text_input],
        outputs=output_audio
    )

demo.launch()