Hello, I'm getting the following error when running interface:

/content/piper/src/python

KeyError Traceback (most recent call last) in <cell line: 374>() 372 return magnitude, phase 373 --> 374 main()

1 frames in inferencing(model, config, sid, line, length_scale, noise_scale, noise_scale_w, auto_play) 244 def inferencing(model, config, sid, line, length_scale = 1, noise_scale = 0.667, noise_scale_w = 0.8, auto_play=True): 245 audios = [] --> 246 if config["phoneme_type"] == "PhonemeType.ESPEAK": 247 config["phoneme_type"] = "espeak" 248 text = phonemize(config, line)

KeyError: 'phoneme_type'

I've solved the problem with the help of ChatGPT, here is the corrected code:

@markdown # Run inference. 🤖

@markdown ---

@markdown #### Before you enjoy... Some notes!

@markdown * You can run the cell to download voice packs and download voices you want at any time, even if you run this cell!

@markdown * When you download a new voice, run this cell again and you will now be able to toggle between all the ones you download. Incredible, right?

@markdown Enjoy!!

%cd /content/piper/src/python

original: infer_onnx.py

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 from piper_train.vits.utils import audio_float_to_int16 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

_LOGGER = logging.getLogger("piper_train.infer_onnx")

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

def main(): """Main entry point""" 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 loadconfig(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 = []

# Check if 'phoneme_type' exists in the config dictionary
phoneme_type = config.get("phoneme_type", PhonemeType.ESPEAK.value)

# Fix applied here
if phoneme_type == PhonemeType.ESPEAK.value:
    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 = [] imagpart = [] for y in x: y = stft(y, fft_size=1024, hopsamp=256).T realpart.append(y.real[None, :, :]) # pylint: disable=unsubscriptable-object imagpart.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

main()

rhasspy / piper

Run inference Error #221

/content/piper/src/python