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 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 IPython.display import display, Audio, Markdown, clear_output from piper_phonemize import phonemize_codepoints, phonemize_espeak, tashkeel_run app = FastAPI() _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 @app.get("/") async def main(): """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) if config["num_speakers"] > 1: speaker_selection.options = config["speaker_id_map"].values() speaker_selection.layout.visibility = 'visible' preview_sid = 0 if enhanced_accessibility: playaudio("multispeaker") else: speaker_selection.layout.visibility = 'hidden' preview_sid = None if enhanced_accessibility: inferencing( model, config, preview_sid, lan.translate( config["espeak"]["voice"][:2], "Interface openned. Write your texts, configure the different synthesis options or download all the voices you want. Enjoy!" ) ) 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) speed_slider = widgets.FloatSlider( value=1, min=0.25, max=4, step=0.1, description=lan.translate(lang, "Rate scale"), orientation='horizontal', ) noise_scale_slider = widgets.FloatSlider( value=0.667, min=0.25, max=4, step=0.1, description=lan.translate(lang, "Phoneme noise scale"), orientation='horizontal', ) noise_scale_w_slider = widgets.FloatSlider( value=1, min=0.25, max=4, step=0.1, description=lan.translate(lang, "Phoneme stressing scale"), orientation='horizontal', ) play = widgets.Checkbox( value=True, description=lan.translate(lang, "Auto-play"), disabled=False ) text_input = widgets.Text( value='', placeholder=f'{lan.translate(lang, "Enter your text here")}:', description=lan.translate(lang, "Text to synthesize"), layout=widgets.Layout(width='80%') ) synthesize_button = widgets.Button( description=lan.translate(lang, "Synthesize"), button_style='success', # 'success', 'info', 'warning', 'danger' or '' tooltip=lan.translate(lang, "Click here to synthesize the text."), icon='check' ) close_button = widgets.Button( description=lan.translate(lang, "Exit"), tooltip=lan.translate(lang, "Closes this GUI."), icon='check' ) def on_synthesize_button_clicked(b): if model is None: if enhanced_accessibility: playaudio("nomodel") raise Exception(lan.translate(lang, "You have not loaded any model from the list!")) text = text_input.value if config["num_speakers"] > 1: sid = speaker_selection.value else: sid = None rate = speed_slider.value noise_scale = noise_scale_slider.value noise_scale_w = noise_scale_w_slider.value auto_play = play.value inferencing(model, config, sid, text, rate, noise_scale, noise_scale_w, auto_play) def on_close_button_clicked(b): clear_output() if enhanced_accessibility: playaudio("exit") synthesize_button.on_click(on_synthesize_button_clicked) close_button.on_click(on_close_button_clicked) display(text_input) display(speed_slider) display(noise_scale_slider) display(noise_scale_w_slider) display(play) display(synthesize_button) display(close_button) 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 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"] 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 #if __name__ == "__main__": # 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!"}