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Updated inference notebook.

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Mateo Cedillo
2023-08-08 15:42:40 -05:00
parent cf892d6e14
commit 91e31de1f2
1 changed files with 67 additions and 31 deletions
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98
notebooks/piper_inference_(ckpt).ipynb
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@@ -5,7 +5,7 @@
"colab": {
"provenance": [],
"gpuType": "T4",
"authorship_tag": "ABX9TyMcevzeVyewWF1ZHKzBu3CB",
"authorship_tag": "ABX9TyNju0yzRK8wgAS+WgyeTEAl",
"include_colab_link": true
},
"kernelspec": {
@@ -88,12 +88,13 @@
" 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 torchtext==0.12.0 torchvision==0.12.0\n",
"#!pip install -q torchtext==0.14.1 torchvision==0.14.1\n",
"# fixing recent compativility isswes:\n",
"!pip install -q torchaudio==0.11.0 torchmetrics==0.11.4\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",
@@ -190,6 +191,8 @@
"import logging\n",
"import sys\n",
"from pathlib import Path\n",
"from enum import Enum\n",
"from typing import Iterable, List, Optional, Union\n",
"import torch\n",
"from piper_train.vits.lightning import VitsModel\n",
"from piper_train.vits.utils import audio_float_to_int16\n",
@@ -198,8 +201,7 @@
"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",
@@ -382,35 +384,69 @@
" config = json.load(file)\n",
" return config\n",
"\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: {self.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 = torch.LongTensor(phoneme_ids).unsqueeze(0)\n",
" text_lengths = torch.LongTensor([len(phoneme_ids)])\n",
" scales = [\n",
" noise_scale,\n",
" length_scale,\n",
" noise_scale_w\n",
" ]\n",
" sid = torch.LongTensor([speaker_id]) if speaker_id is not None else None\n",
" audio = model(\n",
" text,\n",
" text_lengths,\n",
" scales,\n",
" sid=sid\n",
" ).detach().numpy()\n",
" audio = audio_float_to_int16(audio.squeeze())\n",
" audios = []\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 = torch.LongTensor(phoneme_ids).unsqueeze(0)\n",
" text_lengths = torch.LongTensor([len(phoneme_ids)])\n",
" scales = [\n",
" noise_scale,\n",
" length_scale,\n",
" noise_scale_w\n",
" ]\n",
" sid = torch.LongTensor([speaker_id]) if speaker_id is not None else None\n",
" audio = model(\n",
" text,\n",
" text_lengths,\n",
" scales,\n",
" sid=sid\n",
" ).detach().numpy()\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",