app.py

enhanced_accessibility = False #@param {type:"boolean"}
#@markdown ---

#@markdown #### Please select your language:
#lang_select = "English" #@param ["English", "Spanish"]
#if lang_select == "English":
#  lang = "en"
#elif lang_select == "Spanish":
 #   lang = "es"
#else:
#    raise Exception("Language not supported.")
#@markdown ---
lang = "en"
use_gpu = False #@param {type:"boolean"}

from fastapi import FastAPI, Request, Form
from fastapi.responses import HTMLResponse
from fastapi.responses import FileResponse
from fastapi.templating import Jinja2Templates
from fastapi.staticfiles import StaticFiles

# ...
# Mount a directory to serve static files (e.g., CSS and JavaScript)
app.mount("/static", StaticFiles(directory="static"), name="static")

import logging

app = FastAPI()
templates = Jinja2Templates(directory="templates")
files = {}
# Configure logging
logging.basicConfig(level=logging.DEBUG)
# Mock data for your interface
data = {
    "speaker_options": ["Speaker 1", "Speaker 2", "Speaker 3"],
    "default_speaker": "Speaker 1",
}
@app.get("/", response_class=HTMLResponse)
async def read_root(request: Request):
    return templates.TemplateResponse("interface.html", {"request": request, "data": data})

import json
import logging
import math
import sys
from pathlib import Path
from enum import Enum
from typing import Iterable, List, Optional, Union
import numpy as np
import onnxruntime

import glob
import ipywidgets as widgets
from pydub import AudioSegment
import tempfile
import uuid
import soundfile as sf
from IPython.display import display, Audio, Markdown, clear_output
from piper_phonemize import phonemize_codepoints, phonemize_espeak, tashkeel_run


_LOGGER = logging.getLogger("piper_train.infer_onnx")
import os
#if not os.path.exists("./content/piper/src/python/lng"):
 #   import subprocess
 #   command = "cp -r ./content/piper/notebooks/lng ./content/piper/src/python/lng"
 #   subprocess.run(command, shell=True)

import sys
#sys.path.append('/content/piper/notebooks')
sys.path.append('./content/piper/src/python')
import configparser

class Translator:
    def __init__(self):
        self.configs = {}

    def load_language(self, language_name):
        if language_name not in self.configs:
            config = configparser.ConfigParser()
            config.read(os.path.join(os.getcwd(), "lng", f"{language_name}.lang"))
            self.configs[language_name] = config

    def translate(self, language_name, string):
        if language_name == "en":
            return string
        elif language_name not in self.configs:
            self.load_language(language_name)
        config = self.configs[language_name]
        try:
            return config.get("Strings", string)
        except (configparser.NoOptionError, configparser.NoSectionError):
            if string:
                return string
            else:
                raise Exception("language engine error: This translation is corrupt!")
                return 0
#from translator import *
lan = Translator()
def detect_onnx_models(path):
    onnx_models = glob.glob(path + '/*.onnx')
    if len(onnx_models) > 1:
        return onnx_models
    elif len(onnx_models) == 1:
        return onnx_models[0]
    else:
        return None
renamed_audio_file = None

#@app.post("/synthesize")
#@app.post("/", response_class=FileResponse)
@app.post("/", response_class=HTMLResponse)
async def main(
    request: Request,
    text_input: str = Form(...),
    speaker: str = Form(...),
    speed_slider: float = Form(1.0),
    noise_scale_slider: float = Form(0.667),
    noise_scale_w_slider: float = Form(1.0),
    play: bool = Form(True)
):
    """Main entry point"""
    sys.path.append('./content/piper/src/python')
    models_path = "./content/piper/src/python"
    logging.basicConfig(level=logging.DEBUG)
    providers = [
        "CPUExecutionProvider"
        if use_gpu is False
        else ("CUDAExecutionProvider", {"cudnn_conv_algo_search": "DEFAULT"})
    ]
    sess_options = onnxruntime.SessionOptions()
    model = None
    onnx_models = detect_onnx_models(models_path)
    speaker_selection = widgets.Dropdown(
        options=[],
        description=f'{lan.translate(lang, "Select speaker")}:',
        layout={'visibility': 'hidden'}
    )
    if onnx_models is None:
        if enhanced_accessibility:
            playaudio("novoices")
        raise Exception(lan.translate(lang, "No downloaded voice packages!"))
    elif isinstance(onnx_models, str):
        onnx_model = onnx_models
        model, config = load_onnx(onnx_model, sess_options, providers)
        print("nuber of speakers = ", config["num_speakers"])
        print("speaker", speaker)
#        rate = speed_slider.value
 #       noise_scale = noise_scale_slider.value
#        noise_scale_w = noise_scale_w_slider.value
        auto_play = play
        audio = inferencing(model, config, 0, text_input, speed_slider, noise_scale_slider, noise_scale_w_slider, auto_play)
        temp_dir = tempfile.mkdtemp()

        # Create a temporary directory to store the audio files
        #temp_dir = tempfile.mkdtemp()

        # Export the audio to an MP3 file in the temporary directory
       # temp_audio_file = os.path.join(temp_dir, "generated_audio.mp3")
        

        # Rename the audio file based on the text input
        renamed_audio_file = os.path.join(temp_dir, f"{text_input}.mp3")
        audio.export(renamed_audio_file, format="mp3")
        # Save the generated audio as a temporary file
        filepath = renamed_audio_file
    
   # Generate a unique file ID
        file_id = str(uuid.uuid4())
    
    # Store the file path with the generated file ID
        files[file_id] = filepath
    
      # Create a URL to download the file
        file_url = f'/download?fileId={file_id}'
       # os.rename(temp_audio_file, renamed_audio_file)
        # Specify the path to your MP3 audio file
   #     audio_file_path = "path/to/your/audio.mp3"

    # Check if the file exists
 #       if not os.path.exists(audio_file_path):
  #         return {"detail": "Audio file not found"}


#        temp_audio_file = tempfile.NamedTemporaryFile(delete=False, suffix=".mp3")
#        audio.export(temp_audio_file.name, format="mp3")
    
    # Rename the temporary audio file based on the text input
 #       global renamed_audio_file
#        renamed_audio_file = os.path.join(tempfile.gettempdir(), f"{text_input}.mp3")
#        os.rename(temp_audio_file.name, renamed_audio_file)
        if config["num_speakers"] > 1:
            speaker_selection.options = config["speaker_id_map"].values()
            speaker_selection.layout.visibility = 'visible'
            preview_sid = 0
        else:
            speaker_selection.layout.visibility = 'hidden'
            preview_sid = None
    else:
        voice_model_names = []
        for current in onnx_models:
            voice_struct = current.split("/")[5]
            voice_model_names.append(voice_struct)
#        if enhanced_accessibility:
#            playaudio("selectmodel")
#        selection = widgets.Dropdown(
#            options=voice_model_names,
#            description=f'{lan.translate(lang, "Select voice package")}:',
#        )
#        load_btn = widgets.Button(
#            description=lan.translate(lang, "Load it!")
#        )
#       config = None
#        def load_model(button):
 #       nonlocal config
  #      global onnx_model
  #      nonlocal model
   #     nonlocal models_path
   #     selected_voice = selection.value
     #   onnx_model = f"{models_path}/{selected_voice}"
  #      model, config = load_onnx(onnx_model, sess_options, providers)
#            if enhanced_accessibility:
#                playaudio("loaded")
#            if config["num_speakers"] > 1:
#                speaker_selection.options = config["speaker_id_map"].values()
#                speaker_selection.layout.visibility = 'visible'
#                if enhanced_accessibility:
#                    playaudio("multispeaker")
#            else:
#                speaker_selection.layout.visibility = 'hidden'

 #       load_btn.on_click(load_model)
 #       display(selection, load_btn)
#    display(speaker_selection)


       
    

        # Save the audio as a temporary WAV file
    return templates.TemplateResponse("interface.html", {"request": request, "file_url": file_url, "data": data})
   
    # Serve the audio file with the correct media type
 #   return FileResponse(renamed_audio_file)


 #   return {"message": f"Text to synthesize: {text_input}, Speed: {speed_slider}, Play: {play}"}

@app.get("/download/")
async def download_file(fileId: str):
    # Retrieve the file path from the dictionary using the file ID
    filepath = files.get(fileId)
    if filepath:
        # Create a FileResponse to serve the file for download
        return FileResponse(filepath, headers={"Content-Disposition": "attachment"})
    else:
        return {"error": "File not found"}

def load_onnx(model, sess_options, providers = ["CPUExecutionProvider"]):
    _LOGGER.debug("Loading model from %s", model)
    config = load_config(model)
    model = onnxruntime.InferenceSession(
        str(model),
        sess_options=sess_options,
        providers= providers
    )
    _LOGGER.info("Loaded model from %s", model)
    return model, config

def load_config(model):
    with open(f"{model}.json", "r") as file:
        config = json.load(file)
    return config
PAD = "_"  # padding (0)
BOS = "^"  # beginning of sentence
EOS = "$"  # end of sentence

class PhonemeType(str, Enum):
    ESPEAK = "espeak"
    TEXT = "text"

def phonemize(config, text: str) -> List[List[str]]:
    """Text to phonemes grouped by sentence."""
    if config["phoneme_type"] == PhonemeType.ESPEAK:
        if config["espeak"]["voice"] == "ar":
            # Arabic diacritization
            # https://github.com/mush42/libtashkeel/
            text = tashkeel_run(text)
        return phonemize_espeak(text, config["espeak"]["voice"])
    if config["phoneme_type"] == PhonemeType.TEXT:
        return phonemize_codepoints(text)
    raise ValueError(f'Unexpected phoneme type: {config["phoneme_type"]}')

def phonemes_to_ids(config, phonemes: List[str]) -> List[int]:
    """Phonemes to ids."""
    id_map = config["phoneme_id_map"]
    ids: List[int] = list(id_map[BOS])
    for phoneme in phonemes:
        if phoneme not in id_map:
            print("Missing phoneme from id map: %s", phoneme)
            continue
        ids.extend(id_map[phoneme])
        ids.extend(id_map[PAD])
    ids.extend(id_map[EOS])
    return ids
def audio_float_to_int16(
    audio: np.ndarray, max_wav_value: float = 32767.0
) -> np.ndarray:
    """Normalize audio and convert to int16 range"""
    audio_norm = audio * (max_wav_value / max(0.01, np.max(np.abs(audio))))
    audio_norm = np.clip(audio_norm, -max_wav_value, max_wav_value)
    audio_norm = audio_norm.astype("int16")
    return audio_norm
    
def inferencing(model, config, sid, line, length_scale = 1, noise_scale = 0.667, noise_scale_w = 0.8, auto_play=True):
    audios = []
    if config["phoneme_type"] == "PhonemeType.ESPEAK":
        config["phoneme_type"] = "espeak"
    text = phonemize(config, line)
    for phonemes in text:
        phoneme_ids = phonemes_to_ids(config, phonemes)
        num_speakers = config["num_speakers"]
        if num_speakers == 1:
            speaker_id = None # for now
        else:
            speaker_id = sid
        text = np.expand_dims(np.array(phoneme_ids, dtype=np.int64), 0)
        text_lengths = np.array([text.shape[1]], dtype=np.int64)
        scales = np.array(
            [noise_scale, length_scale, noise_scale_w],
            dtype=np.float32,
        )
        sid = None
        if speaker_id is not None:
            sid = np.array([speaker_id], dtype=np.int64)
        audio = model.run(
            None,
            {
                "input": text,
                "input_lengths": text_lengths,
                "scales": scales,
                "sid": sid,
            },
        )[0].squeeze((0, 1))
        audio = audio_float_to_int16(audio.squeeze())
        audios.append(audio)
    merged_audio = np.concatenate(audios)
    sample_rate = config["audio"]["sample_rate"]
    temp_audio_path = os.path.join(tempfile.gettempdir(), "generated_audio.wav")
    sf.write(temp_audio_path, merged_audio, config["audio"]["sample_rate"])
    audio = AudioSegment.from_mp3(temp_audio_path)
    return audio
#    return FileResponse(temp_audio_path)
    # Return the audio file as a FastAPI response
  #  display(Markdown(f"{line}"))
   # display(Audio(merged_audio, rate=sample_rate, autoplay=auto_play))

def denoise(
    audio: np.ndarray, bias_spec: np.ndarray, denoiser_strength: float
) -> np.ndarray:
    audio_spec, audio_angles = transform(audio)

    a = bias_spec.shape[-1]
    b = audio_spec.shape[-1]
    repeats = max(1, math.ceil(b / a))
    bias_spec_repeat = np.repeat(bias_spec, repeats, axis=-1)[..., :b]

    audio_spec_denoised = audio_spec - (bias_spec_repeat * denoiser_strength)
    audio_spec_denoised = np.clip(audio_spec_denoised, a_min=0.0, a_max=None)
    audio_denoised = inverse(audio_spec_denoised, audio_angles)

    return audio_denoised


def stft(x, fft_size, hopsamp):
    """Compute and return the STFT of the supplied time domain signal x.
    Args:
        x (1-dim Numpy array): A time domain signal.
        fft_size (int): FFT size. Should be a power of 2, otherwise DFT will be used.
        hopsamp (int):
    Returns:
        The STFT. The rows are the time slices and columns are the frequency bins.
    """
    window = np.hanning(fft_size)
    fft_size = int(fft_size)
    hopsamp = int(hopsamp)
    return np.array(
        [
            np.fft.rfft(window * x[i : i + fft_size])
            for i in range(0, len(x) - fft_size, hopsamp)
        ]
    )


def istft(X, fft_size, hopsamp):
    """Invert a STFT into a time domain signal.
    Args:
        X (2-dim Numpy array): Input spectrogram. The rows are the time slices and columns are the frequency bins.
        fft_size (int):
        hopsamp (int): The hop size, in samples.
    Returns:
        The inverse STFT.
    """
    fft_size = int(fft_size)
    hopsamp = int(hopsamp)
    window = np.hanning(fft_size)
    time_slices = X.shape[0]
    len_samples = int(time_slices * hopsamp + fft_size)
    x = np.zeros(len_samples)
    for n, i in enumerate(range(0, len(x) - fft_size, hopsamp)):
        x[i : i + fft_size] += window * np.real(np.fft.irfft(X[n]))
    return x


def inverse(magnitude, phase):
    recombine_magnitude_phase = np.concatenate(
        [magnitude * np.cos(phase), magnitude * np.sin(phase)], axis=1
    )

    x_org = recombine_magnitude_phase
    n_b, n_f, n_t = x_org.shape  # pylint: disable=unpacking-non-sequence
    x = np.empty([n_b, n_f // 2, n_t], dtype=np.complex64)
    x.real = x_org[:, : n_f // 2]
    x.imag = x_org[:, n_f // 2 :]
    inverse_transform = []
    for y in x:
        y_ = istft(y.T, fft_size=1024, hopsamp=256)
        inverse_transform.append(y_[None, :])

    inverse_transform = np.concatenate(inverse_transform, 0)

    return inverse_transform


def transform(input_data):
    x = input_data
    real_part = []
    imag_part = []
    for y in x:
        y_ = stft(y, fft_size=1024, hopsamp=256).T
        real_part.append(y_.real[None, :, :])  # pylint: disable=unsubscriptable-object
        imag_part.append(y_.imag[None, :, :])  # pylint: disable=unsubscriptable-object
    real_part = np.concatenate(real_part, 0)
    imag_part = np.concatenate(imag_part, 0)

    magnitude = np.sqrt(real_part**2 + imag_part**2)
    phase = np.arctan2(imag_part.data, real_part.data)

    return magnitude, phase



#@app.get("/")
#async def read_root(request: Request):
#   return templates.TemplateResponse("interface.html", {"request": request})

if __name__ == "__main__":
 #   main()
    import uvicorn
    uvicorn.run(app, host="0.0.0.0", port=7860)
#    main()
#    pass
   # app()  
    
# Create an instance of the FastAPI class
#app = main()

# Define a route for the root endpoint

#def read_root():
#  return {"message": "Hello, World!"}