{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import os\n", "os.environ[\"CUDA_DEVICE_ORDER\"]=\"PCI_BUS_ID\" \n", "os.environ[\"CUDA_VISIBLE_DEVICES\"]=\"0\"" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/pyp/miniconda3/envs/voicecraft/lib/python3.9/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", " from .autonotebook import tqdm as notebook_tqdm\n" ] } ], "source": [ "# import libs\n", "import torch\n", "import torchaudio\n", "\n", "from data.tokenizer import (\n", " AudioTokenizer,\n", " TextTokenizer,\n", ")\n", "\n", "\n" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "# hyperparameters for inference\n", "left_margin = 0.08\n", "right_margin = 0.08\n", "seed = 1\n", "codec_audio_sr = 16000\n", "codec_sr = 50\n", "top_k = 0\n", "top_p = 0.8\n", "temperature = 1\n", "kvcache = 0\n", "silence_tokens=[1388,1898,131]\n", "# if there are long silence in the generated audio, reduce the stop_repetition to 3, 2 or even 1\n", "stop_repetition = 2\n", "# if there are long silence or unnaturally strecthed words, increase sample_batch_size to 2, 3 or even 4\n", "# 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\n", "sample_batch_size = 1\n", "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n", "\n", "# point to the original file or record the file\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 = \"/home/pyp/VoiceCraft/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 = \"/home/pyp/VoiceCraft/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", "os.makedirs(align_temp, exist_ok=True)\n", "os.system(f\"mfa align -j 1 --output_format csv {temp_folder} english_us_arpa english_us_arpa {align_temp}\")\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", "# os.system(f\"mfa align -j 1 --output_format csv {temp_folder} english_us_arpa english_us_arpa {align_temp} --beam 1000 --retry_beam 2000\")\n", "audio_fn = f\"{temp_folder}/{filename}.wav\"\n", "transcript_fn = f\"{temp_folder}/{filename}.txt\"\n", "align_fn = f\"{align_temp}/{filename}.csv\"" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Dora directory: /tmp/audiocraft_pyp\n" ] } ], "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 # according to forced-alignment file, the word \"common\" stop as 3.01 sec\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", "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", "\n", "# # load model, tokenizer, and other necessary files\n", "from models import voicecraft\n", "ckpt_fn = \"/data/scratch/pyp/exp_pyp/VoiceCraft/gigaspeech/pretrained_830M/best_bundle.pth\"\n", "encodec_fn = \"/data/scratch/pyp/exp_pyp/audiocraft/encodec/xps/6f79c6a8/checkpoint.th\"\n", "ckpt = torch.load(ckpt_fn, map_location=\"cpu\")\n", "model = voicecraft.VoiceCraft(ckpt[\"config\"])\n", "model.load_state_dict(ckpt[\"model\"])\n", "model.to(device)\n", "model.eval()\n", "\n", "phn2num = ckpt['phn2num']\n", "\n", "text_tokenizer = TextTokenizer(backend=\"espeak\")\n", "audio_tokenizer = AudioTokenizer(signature=encodec_fn) # will also put the neural codec model on gpu\n", "\n", "# run the model to get the output\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, ckpt[\"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", "# output_dir\n", "output_dir = \"/home/pyp/VoiceCraft/demo/generated_tts\"\n", "os.makedirs(output_dir, exist_ok=True)\n", "\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", "# if you get error importing T5 in transformers\n", "# try \n", "# pip uninstall Pillow\n", "# pip install Pillow\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": 2 }