@ -5,7 +5,7 @@
"colab": {
"provenance": [],
"gpuType": "T4",
"authorship_tag": "ABX9TyPFgeWX60dXmmKm+pi5Wr2v ",
"authorship_tag": "ABX9TyMAPvo6Syxu5wDRkSmySUxq ",
"include_colab_link": true
},
"kernelspec": {
@ -14,7 +14,8 @@
},
"language_info": {
"name": "python"
}
},
"accelerator": "GPU"
},
"cells": [
{
@ -74,7 +75,7 @@
"#@markdown #### Do you want to use the GPU for inference?\n",
"\n",
"#@markdown The GPU can be enabled in the edit/notebook settings menu, and this step must be done before connecting to a runtime. The GPU can lead to a higher response speed in inference, but you can use the CPU, for example, if your colab runtime to use GPU's has been ended.\n",
"use_gpu = Fals e #@param {type:\"boolean\"}\n",
"use_gpu = Tru e #@param {type:\"boolean\"}\n",
"\n",
"if enhanced_accessibility:\n",
" from google.colab import output\n",
@ -88,10 +89,10 @@
" playaudio(\"installing\")\n",
"!git clone -q https://github.com/rmcpantoja/piper\n",
"%cd /content/piper/src/python\n",
"!pip install -q -r requirements.txt\n",
"#!pip install -q -r requirements.txt\n",
"!pip install -q cython>=0.29.0 piper-phonemize==1.1.0 librosa>=0.9.2 numpy>=1.19.0 onnxruntime>=1.11.0 pytorch-lightning==1.7.0 torch==1.11.0\n",
"!pip install -q onnxruntime-gpu\n",
"!bash build_monotonic_align.sh\n",
"!apt-get install -q espeak-ng\n",
"import os\n",
"if not os.path.exists(\"/content/piper/src/python/lng\"):\n",
" !cp -r \"/content/piper/notebooks/lng\" /content/piper/src/python/lng\n",
@ -186,15 +187,15 @@
"import math\n",
"import sys\n",
"from pathlib import Path\n",
"\n",
"from enum import Enum\n",
"from typing import Iterable, List, Optional, Union\n",
"import numpy as np\n",
"import onnxruntime\n",
"from piper_train.vits.utils import audio_float_to_int16\n",
"import glob\n",
"import ipywidgets as widgets\n",
"from IPython.display import display, Audio, Markdown, clear_output\n",
"from espeak_phonemizer import Phonemizer\n",
"from piper_train import phonemize\n",
"from piper_phonemize import phonemize_codepoints, phonemize_espeak, tashkeel_run\n",
"\n",
"_LOGGER = logging.getLogger(\"piper_train.infer_onnx\")\n",
"\n",
@ -381,40 +382,75 @@
" with open(f\"{model}.json\", \"r\") as file:\n",
" config = json.load(file)\n",
" return config\n",
"PAD = \"_\" # padding (0)\n",
"BOS = \"^\" # beginning of sentence\n",
"EOS = \"$\" # end of sentence\n",
"\n",
"class PhonemeType(str, Enum):\n",
" ESPEAK = \"espeak\"\n",
" TEXT = \"text\"\n",
"\n",
"def phonemize(config, text: str) -> List[List[str]]:\n",
" \"\"\"Text to phonemes grouped by sentence.\"\"\"\n",
" if config[\"phoneme_type\"] == PhonemeType.ESPEAK:\n",
" if config[\"espeak\"][\"voice\"] == \"ar\":\n",
" # Arabic diacritization\n",
" # https://github.com/mush42/libtashkeel/\n",
" text = tashkeel_run(text)\n",
" return phonemize_espeak(text, config[\"espeak\"][\"voice\"])\n",
" if config[\"phoneme_type\"] == PhonemeType.TEXT:\n",
" return phonemize_codepoints(text)\n",
" raise ValueError(f'Unexpected phoneme type: {config[\"phoneme_type\"]}')\n",
"\n",
"def phonemes_to_ids(config, phonemes: List[str]) -> List[int]:\n",
" \"\"\"Phonemes to ids.\"\"\"\n",
" id_map = config[\"phoneme_id_map\"]\n",
" ids: List[int] = list(id_map[BOS])\n",
" for phoneme in phonemes:\n",
" if phoneme not in id_map:\n",
" print(\"Missing phoneme from id map: %s\", phoneme)\n",
" continue\n",
" ids.extend(id_map[phoneme])\n",
" ids.extend(id_map[PAD])\n",
" ids.extend(id_map[EOS])\n",
" return ids\n",
"\n",
"def inferencing(model, config, sid, line, length_scale = 1, noise_scale = 0.667, noise_scale_w = 0.8, auto_play=True):\n",
" espeak_voice = config[\"espeak\"][\"voice\"]\n",
" phonemizer = Phonemizer(default_voice=espeak_voice)\n",
" phonemes = phonemize.phonemize(line, phonemizer)\n",
" ids = phonemize.phonemes_to_ids(phonemes)\n",
" phoneme_ids = ids\n",
" num_speakers = config[\"num_speakers\"]\n",
" if num_speakers == 1:\n",
" speaker_id = None # for now\n",
" else:\n",
" speaker_id = sid\n",
" text = np.expand_dims(np.array(phoneme_ids, dtype=np.int64), 0)\n",
" text_lengths = np.array([text.shape[1]], dtype=np.int64)\n",
" scales = np.array(\n",
" [noise_scale, length_scale, noise_scale_w],\n",
" dtype=np.float32,\n",
" )\n",
" sid = None\n",
" if speaker_id is not None:\n",
" sid = np.array([speaker_id], dtype=np.int64)\n",
" audio = model.run(\n",
" None,\n",
" {\n",
" \"input\": text,\n",
" \"input_lengths\": text_lengths,\n",
" \"scales\": scales,\n",
" \"sid\": sid,\n",
" },\n",
" )[0].squeeze((0, 1))\n",
" audio = audio_float_to_int16(audio.squeeze())\n",
" audios = []\n",
" if config[\"phoneme_type\"] == \"PhonemeType.ESPEAK\":\n",
" config[\"phoneme_type\"] = \"espeak\"\n",
" text = phonemize(config, line)\n",
" for phonemes in text:\n",
" phoneme_ids = phonemes_to_ids(config, phonemes)\n",
" num_speakers = config[\"num_speakers\"]\n",
" if num_speakers == 1:\n",
" speaker_id = None # for now\n",
" else:\n",
" speaker_id = sid\n",
" text = np.expand_dims(np.array(phoneme_ids, dtype=np.int64), 0)\n",
" text_lengths = np.array([text.shape[1]], dtype=np.int64)\n",
" scales = np.array(\n",
" [noise_scale, length_scale, noise_scale_w],\n",
" dtype=np.float32,\n",
" )\n",
" sid = None\n",
" if speaker_id is not None:\n",
" sid = np.array([speaker_id], dtype=np.int64)\n",
" audio = model.run(\n",
" None,\n",
" {\n",
" \"input\": text,\n",
" \"input_lengths\": text_lengths,\n",
" \"scales\": scales,\n",
" \"sid\": sid,\n",
" },\n",
" )[0].squeeze((0, 1))\n",
" audio = audio_float_to_int16(audio.squeeze())\n",
" audios.append(audio)\n",
" merged_audio = np.concatenate(audios)\n",
" sample_rate = config[\"audio\"][\"sample_rate\"]\n",
" display(Markdown(f\"{line}\"))\n",
" display(Audio(audio, rate=sample_rate, autoplay=auto_play))\n",
" display(Audio(merged_ audio, rate=sample_rate, autoplay=auto_play))\n",
"\n",
"def denoise(\n",
" audio: np.ndarray, bias_spec: np.ndarray, denoiser_strength: float\n",
@ -512,8 +548,7 @@
"main()"
],
"metadata": {
"id": "hcKk8M2ug8kM",
"cellView": "form"
"id": "hcKk8M2ug8kM"
},
"execution_count": null,
"outputs": []
Mateo Cedillo
10 months ago