{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "VoiceCraft Inference Text To Speech Demo\n", "===" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Select 'voicecraft' as the kernel" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "# import libs\n", "# if this throws an error, something went wrong installing dependencies or changing the kernel above!\n", "import os\n", "os.environ[\"CUDA_DEVICE_ORDER\"]=\"PCI_BUS_ID\" \n", "os.environ[\"CUDA_VISIBLE_DEVICES\"]=\"0\"\n", "os.environ[\"USER\"] = \"me\" # TODO change this to your username\n", "\n", "import torch\n", "import torchaudio\n", "import numpy as np\n", "import random\n", "from argparse import Namespace\n", "\n", "from data.tokenizer import (\n", " AudioTokenizer,\n", " TextTokenizer,\n", ")\n", "from huggingface_hub import hf_hub_download" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "# # install MFA models and dictionaries if you haven't done so already, already done in the dockerfile or envrionment setup\n", "# !source ~/.bashrc && \\\n", "# conda activate voicecraft && \\\n", "# mfa model download dictionary english_us_arpa && \\\n", "# mfa model download acoustic english_us_arpa" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Dora directory: /tmp/audiocraft_me\n" ] } ], "source": [ "# load model, encodec, and phn2num\n", "# # load model, tokenizer, and other necessary files\n", "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n", "voicecraft_name=\"giga330M.pth\" # or gigaHalfLibri330M_TTSEnhanced_max16s.pth, giga830M.pth\n", "\n", "# the new way of loading the model, with huggingface, recommended\n", "from models import voicecraft\n", "model = voicecraft.VoiceCraft.from_pretrained(f\"pyp1/VoiceCraft_{voicecraft_name.replace('.pth', '')}\")\n", "phn2num = model.args.phn2num\n", "config = vars(model.args)\n", "model.to(device)\n", "\n", "\n", "# # the old way of loading the model\n", "# from models import voicecraft\n", "# filepath = hf_hub_download(repo_id=\"pyp1/VoiceCraft\", filename=voicecraft_name, repo_type=\"model\")\n", "# ckpt = torch.load(filepath, map_location=\"cpu\")\n", "# model = voicecraft.VoiceCraft(ckpt[\"config\"])\n", "# model.load_state_dict(ckpt[\"model\"])\n", "# config = vars(model.args)\n", "# phn2num = ckpt[\"phn2num\"]\n", "# model.to(device)\n", "# model.eval()\n", "\n", "\n", "encodec_fn = \"./pretrained_models/encodec_4cb2048_giga.th\"\n", "if not os.path.exists(encodec_fn):\n", " os.system(f\"wget https://huggingface.co/pyp1/VoiceCraft/resolve/main/encodec_4cb2048_giga.th\")\n", " os.system(f\"mv encodec_4cb2048_giga.th ./pretrained_models/encodec_4cb2048_giga.th\")\n", "audio_tokenizer = AudioTokenizer(signature=encodec_fn, device=device) # will also put the neural codec model on gpu\n", "\n", "text_tokenizer = TextTokenizer(backend=\"espeak\")\n" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "# Prepare your audio\n", "# point to the original audio whose speech you want to clone\n", "# write down the transcript for the file, or run whisper to get the transcript (and you can modify it if it's not accurate), save it as a .txt file\n", "orig_audio = \"./demo/84_121550_000074_000000.wav\"\n", "orig_transcript = \"But when I had approached so near to them The common object, which the sense deceives, Lost not by distance any of its marks,\"\n", "\n", "# move the audio and transcript to temp folder\n", "temp_folder = \"./demo/temp\"\n", "os.makedirs(temp_folder, exist_ok=True)\n", "os.system(f\"cp {orig_audio} {temp_folder}\")\n", "filename = os.path.splitext(orig_audio.split(\"/\")[-1])[0]\n", "with open(f\"{temp_folder}/{filename}.txt\", \"w\") as f:\n", " f.write(orig_transcript)\n", "# run MFA to get the alignment\n", "align_temp = f\"{temp_folder}/mfa_alignments\"\n", "!source ~/.bashrc && \\\n", " conda activate voicecraft && \\\n", " mfa align -v --clean -j 1 --output_format csv {temp_folder} \\\n", " english_us_arpa english_us_arpa {align_temp}\n", "\n", "# # if the above fails, it could be because the audio is too hard for the alignment model, increasing the beam size usually solves the issue\n", "# !source ~/.bashrc && \\\n", "# conda activate voicecraft && \\\n", "# mfa align -v --clean -j 1 --output_format csv {temp_folder} \\\n", "# english_us_arpa english_us_arpa {align_temp} --beam 1000 --retry_beam 2000\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# take a look at demo/temp/mfa_alignment, decide which part of the audio to use as prompt\n", "cut_off_sec = 3.01 # NOTE: according to forced-alignment file demo/temp/mfa_alignments/84_121550_000074_000000.csv, the word \"common\" stop as 3.01 sec, this should be different for different audio\n", "target_transcript = \"But when I had approached so near to them The common I cannot believe that the same model can also do text to speech synthesis as well!\"\n", "# NOTE: 3 sec of reference is generally enough for high quality voice cloning, but longer is generally better, try e.g. 3~6 sec.\n", "audio_fn = f\"{temp_folder}/{filename}.wav\"\n", "info = torchaudio.info(audio_fn)\n", "audio_dur = info.num_frames / info.sample_rate\n", "\n", "assert cut_off_sec < audio_dur, f\"cut_off_sec {cut_off_sec} is larger than the audio duration {audio_dur}\"\n", "prompt_end_frame = int(cut_off_sec * info.sample_rate)\n", "\n", "# run the model to get the output\n", "# hyperparameters for inference\n", "codec_audio_sr = 16000\n", "codec_sr = 50\n", "top_k = 0\n", "top_p = 0.9 # can also try 0.8, but 0.9 seems to work better\n", "temperature = 1\n", "silence_tokens=[1388,1898,131]\n", "kvcache = 1 # NOTE if OOM, change this to 0, or try the 330M model\n", "\n", "# NOTE adjust the below three arguments if the generation is not as good\n", "stop_repetition = 3 # NOTE if the model generate long silence, reduce the stop_repetition to 3, 2 or even 1\n", "sample_batch_size = 5 # NOTE: if the if there are long silence or unnaturally strecthed words, increase sample_batch_size to 5 or higher. What this will do to the model is that the model will run sample_batch_size examples of the same audio, and pick the one that's the shortest. So if the speech rate of the generated is too fast change it to a smaller number.\n", "seed = 1 # change seed if you are still unhappy with the result\n", "\n", "def seed_everything(seed):\n", " os.environ['PYTHONHASHSEED'] = str(seed)\n", " random.seed(seed)\n", " np.random.seed(seed)\n", " torch.manual_seed(seed)\n", " torch.cuda.manual_seed(seed)\n", " torch.backends.cudnn.benchmark = False\n", " torch.backends.cudnn.deterministic = True\n", "seed_everything(seed)\n", "\n", "decode_config = {'top_k': top_k, 'top_p': top_p, 'temperature': temperature, 'stop_repetition': stop_repetition, 'kvcache': kvcache, \"codec_audio_sr\": codec_audio_sr, \"codec_sr\": codec_sr, \"silence_tokens\": silence_tokens, \"sample_batch_size\": sample_batch_size}\n", "from inference_tts_scale import inference_one_sample\n", "concated_audio, gen_audio = inference_one_sample(model, Namespace(**config), phn2num, text_tokenizer, audio_tokenizer, audio_fn, target_transcript, device, decode_config, prompt_end_frame)\n", " \n", "# save segments for comparison\n", "concated_audio, gen_audio = concated_audio[0].cpu(), gen_audio[0].cpu()\n", "# logging.info(f\"length of the resynthesize orig audio: {orig_audio.shape}\")\n", "\n", "\n", "# display the audio\n", "from IPython.display import Audio\n", "print(\"concatenate prompt and generated:\")\n", "display(Audio(concated_audio, rate=codec_audio_sr))\n", "\n", "print(\"generated:\")\n", "display(Audio(gen_audio, rate=codec_audio_sr))\n", "\n", "# # save the audio\n", "# # output_dir\n", "# output_dir = \"/home/pyp/VoiceCraft/demo/generated_tts\"\n", "# os.makedirs(output_dir, exist_ok=True)\n", "# seg_save_fn_gen = f\"{output_dir}/{os.path.basename(audio_fn)[:-4]}_gen_seed{seed}.wav\"\n", "# seg_save_fn_concat = f\"{output_dir}/{os.path.basename(audio_fn)[:-4]}_concat_seed{seed}.wav\" \n", "\n", "# torchaudio.save(seg_save_fn_gen, gen_audio, codec_audio_sr)\n", "# torchaudio.save(seg_save_fn_concat, concated_audio, codec_audio_sr)\n", "\n", "# you are might get warnings like WARNING:phonemizer:words count mismatch on 300.0% of the lines (3/1), this can be safely ignored" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "voicecraft", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.18" } }, "nbformat": 4, "nbformat_minor": 4 }