app.py

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

title = """# 🙋🏻‍♂️ Welcome to🌟Collabora🌬️💬📝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. Install it and use your command line interface locally with `pip install whisperspeech`. It's like Stable Diffusion but for speech – both powerful and easily customizable : so you can use it programmatically in your own pipelines! [Contribute to whisperspeech here](https://github.com/collabora/WhisperSpeech) 
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> 

We're **celebrating the release of the whisperspeech** at [the LAION community, if you love open source ai learn more here : https://laion.ai/](https://laion.ai/) big thanks to the folks at huggingface for the community grant 🤗

### How to Use
Input text with tahe language identifiers provided to create a multilingual speech. Optionally you can add an audiosample to make a voice print.Scroll down and try the api <3 Gradio.
This space runs on ZeroGPU, so **you need to be patient** while you acquire the GPU and load the model the first time you make a request !
"""


text_examples = [
    ["<en> WhisperSpeech is an opensource library that helps you hack whisper."],
    ["<de> WhisperSpeech is multi-lingual <es> y puede cambiar de idioma <hi> मध्य वाक्य में"],
    ["<en> The big difference between Europe <fr> et les Etats Unis <pl> jest to, że mamy tak wiele języków <uk> тут, в Європі"]
]

# Function to parse the multilingual input text
def parse_multilingual_text(input_text):
    pattern = r"<(\w+)>\s(.*?)\s(?=<\w+>|$)"
    segments = re.findall(pattern, input_text)
    return [(lang, text.strip()) for lang, text in segments if lang in LANGUAGES.keys()]

@spaces.GPU
def generate_segment_audio(text, lang, speaker_url, pipe):
    if not isinstance(text, str):
        text = text.decode("utf-8") if isinstance(text, bytes) else str(text)
    audio_data = pipe.generate(text, speaker_url, lang)
    resample_audio = resampler(newsr=24000)
    audio_data_resampled = next(resample_audio([{'sample_rate': 24000, 'samples': audio_data.cpu()}]))['samples_24k']
    audio_np = audio_data_resampled.cpu().numpy()
    print("Shape after resampling:", audio_np.shape)  # Debug statement
    return audio_np

def concatenate_audio_segments(segments):
    concatenated_audio = np.concatenate(segments , axis=1)
    return concatenated_audio


@spaces.GPU
def whisper_speech_demo(multilingual_text, speaker_audio):
    segments = parse_multilingual_text(multilingual_text)
    if not segments:
        return None, "No valid language segments found. Please use the format: <lang> text"

    pipe = Pipeline()
    speaker_url = speaker_audio if speaker_audio is not None else None
    audio_segments = []

    for lang, text in segments:
        text_str = text if isinstance(text, str) else str(text)
        audio_np = generate_segment_audio(text_str, lang, speaker_url, pipe)
        print("Audio segment shape:", audio_np.shape)  # Debug statement
        audio_segments.append(audio_np)

    concatenated_audio = concatenate_audio_segments(audio_segments)
    print("Final concatenated audio shape:", concatenated_audio.shape)  # Debug statement
    concatenated_audio = concatenated_audio / np.max(np.abs(concatenated_audio))

    with tempfile.NamedTemporaryFile(suffix='.wav', delete=False) as tmp_file:
        sf.write(tmp_file.name, concatenated_audio.T, 24000, format='WAV', subtype='PCM_16')
        return tmp_file.name

with gr.Blocks() as demo:
    gr.Markdown(title)
    output_audio = gr.Audio(label="🌟Collabora🌬️💬📝WhisperSpeech")
    generate_button = gr.Button("Try 🌟Collabora🌬️💬📝WhisperSpeech")
    with gr.Row():
        text_input = gr.Textbox(label="Enter multilingual text💬📝", placeholder="e.g., <en> Hello <fr> Bonjour <es> Hola")
        speaker_input = gr.Audio(label="Upload or Record Speaker Audio (optional)🌬️💬", sources=["upload", "microphone"])
    with gr.Row():
        with gr.Accordion("Available Languages and Their Tags", open=False):
            formatted_language_list = "\n".join([f"<{lang}> {LANGUAGES[lang]}" for lang in LANGUAGES])
            gr.Markdown(formatted_language_list)
    with gr.Row():
        with gr.Accordion("Try Multilingual Text Examples", open=False):
            gr.Examples(
                examples=text_examples,
                inputs=[text_input],
                outputs=[output_audio],
                fn=whisper_speech_demo,
                cache_examples=True,
                label="Try these to get started !🌟🌬️"
            )
    generate_button.click(whisper_speech_demo, inputs=[text_input, speaker_input], outputs=output_audio)

demo.launch()