210 lines
8.3 KiB
Plaintext
210 lines
8.3 KiB
Plaintext
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"import os\n",
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"os.environ[\"CUDA_DEVICE_ORDER\"]=\"PCI_BUS_ID\" \n",
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"os.environ[\"CUDA_VISIBLE_DEVICES\"]=\"0\""
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"/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",
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" from .autonotebook import tqdm as notebook_tqdm\n"
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]
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}
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],
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"source": [
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"# import libs\n",
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"import torch\n",
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"import torchaudio\n",
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"\n",
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"from data.tokenizer import (\n",
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" AudioTokenizer,\n",
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" TextTokenizer,\n",
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")\n",
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"\n",
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"from models import voicecraft\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [],
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"source": [
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"# hyperparameters for inference\n",
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"left_margin = 0.08\n",
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"right_margin = 0.08\n",
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"seed = 1\n",
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"codec_audio_sr = 16000\n",
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"codec_sr = 50\n",
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"top_k = 0\n",
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"top_p = 0.8\n",
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"temperature = 1\n",
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"kvcache = 0\n",
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"silence_tokens = [1388,1898,131]\n",
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"stop_repetition = -1 # do not stop repetition on silence\n",
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"device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
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"\n",
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"# point to the original file or record the file\n",
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"# 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",
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"orig_audio = \"./demo/84_121550_000074_000000.wav\"\n",
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"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",
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"# move the audio and transcript to temp folder\n",
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"temp_folder = \"./demo/temp\"\n",
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"os.makedirs(temp_folder, exist_ok=True)\n",
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"os.system(f\"cp {orig_audio} {temp_folder}\")\n",
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"filename = os.path.splitext(orig_audio.split(\"/\")[-1])[0]\n",
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"with open(f\"{temp_folder}/{filename}.txt\", \"w\") as f:\n",
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" f.write(orig_transcript)\n",
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"# run MFA to get the alignment\n",
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"align_temp = f\"{temp_folder}/mfa_alignments\"\n",
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"os.makedirs(align_temp, exist_ok=True)\n",
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"os.system(f\"mfa align -j 1 --output_format csv {temp_folder} english_us_arpa english_us_arpa {align_temp}\")\n",
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"# if it fail, it could be because the audio is too hard for the alignment model, increasing the beam size usually solves the issue\n",
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"# 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",
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"audio_fn = f\"{temp_folder}/{filename}.wav\"\n",
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"transcript_fn = f\"{temp_folder}/{filename}.txt\"\n",
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"align_fn = f\"{align_temp}/{filename}.csv\"\n",
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"\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"WARNING:phonemizer:words count mismatch on 300.0% of the lines (3/1)\n"
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]
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}
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],
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"source": [
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"editTypes_set = set(['substitution', 'insertion', 'deletion'])\n",
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"# propose what do you want the target modified transcript to be\n",
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"target_transcript = \"But when I saw the mirage of the lake in the distance, which the sense deceives, Lost not by distance any of its marks,\"\n",
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"edit_type = \"substitution\"\n",
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"assert edit_type in editTypes_set, f\"Invalid edit type {edit_type}. Must be one of {editTypes_set}.\"\n",
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"\n",
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"# if you want to do a second modification on top of the first one, write down the second modification (target_transcript2, type_of_modification2)\n",
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"# make sure the two modification do not overlap, if they do, you need to combine them into one modification\n",
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"\n",
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"# run the script to turn user input to the format that the model can take\n",
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"from edit_utils import get_span\n",
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"orig_span, new_span = get_span(orig_transcript, target_transcript, edit_type)\n",
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"if orig_span[0] > orig_span[1]:\n",
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" RuntimeError(f\"example {audio_fn} failed\")\n",
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"if orig_span[0] == orig_span[1]:\n",
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" orig_span_save = [orig_span[0]]\n",
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"else:\n",
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" orig_span_save = orig_span\n",
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"if new_span[0] == new_span[1]:\n",
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" new_span_save = [new_span[0]]\n",
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"else:\n",
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" new_span_save = new_span\n",
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"\n",
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"orig_span_save = \",\".join([str(item) for item in orig_span_save])\n",
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"new_span_save = \",\".join([str(item) for item in new_span_save])\n",
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"from inference_speech_editing_scale import get_mask_interval\n",
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"\n",
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"start, end = get_mask_interval(align_fn, orig_span_save, edit_type)\n",
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"info = torchaudio.info(audio_fn)\n",
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"audio_dur = info.num_frames / info.sample_rate\n",
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"morphed_span = (max(start - left_margin, 1/codec_sr), min(end + right_margin, audio_dur)) # in seconds\n",
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"\n",
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"# span in codec frames\n",
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"mask_interval = [[round(morphed_span[0]*codec_sr), round(morphed_span[1]*codec_sr)]]\n",
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"mask_interval = torch.LongTensor(mask_interval) # [M,2], M==1 for now\n",
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"\n",
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"# load model, tokenizer, and other necessary files\n",
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"ckpt_fn = \"/data/scratch/pyp/exp_pyp/VoiceCraft/gigaspeech/pretrained_830M/best_bundle.pth\"\n",
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"encodec_fn = \"/data/scratch/pyp/exp_pyp/audiocraft/encodec/xps/6f79c6a8/checkpoint.th\"\n",
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"ckpt = torch.load(ckpt_fn, map_location=\"cpu\")\n",
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"model = voicecraft.VoiceCraft(ckpt[\"config\"])\n",
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"model.load_state_dict(ckpt[\"model\"])\n",
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"model.to(device)\n",
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"model.eval()\n",
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"\n",
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"phn2num = ckpt['phn2num']\n",
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"\n",
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"text_tokenizer = TextTokenizer(backend=\"espeak\")\n",
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"audio_tokenizer = AudioTokenizer(signature=encodec_fn) # will also put the neural codec model on gpu\n",
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"\n",
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"# run the model to get the output\n",
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"from inference_speech_editing_scale import inference_one_sample\n",
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"\n",
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"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}\n",
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"orig_audio, new_audio = inference_one_sample(model, ckpt[\"config\"], phn2num, text_tokenizer, audio_tokenizer, audio_fn, target_transcript, mask_interval, device, decode_config)\n",
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" \n",
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"# save segments for comparison\n",
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"orig_audio, new_audio = orig_audio[0].cpu(), new_audio[0].cpu()\n",
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"# logging.info(f\"length of the resynthesize orig audio: {orig_audio.shape}\")\n",
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"\n",
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"# output_dir\n",
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"output_dir = \"./demo/generated_se\"\n",
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"os.makedirs(output_dir, exist_ok=True)\n",
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"\n",
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"save_fn_new = f\"{output_dir}/{os.path.basename(audio_fn)[:-4]}_new_seed{seed}.wav\"\n",
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"\n",
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"torchaudio.save(save_fn_new, new_audio, codec_audio_sr)\n",
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"\n",
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"save_fn_orig = f\"{output_dir}/{os.path.basename(audio_fn)[:-4]}_orig.wav\"\n",
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"if not os.path.isfile(save_fn_orig):\n",
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" orig_audio, orig_sr = torchaudio.load(audio_fn)\n",
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" if orig_sr != codec_audio_sr:\n",
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" orig_audio = torchaudio.transforms.Resample(orig_sr, codec_audio_sr)(orig_audio)\n",
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" torchaudio.save(save_fn_orig, orig_audio, codec_audio_sr)\n",
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"\n",
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"# if you get error importing T5 in transformers\n",
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"# try \n",
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"# pip uninstall Pillow\n",
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"# pip install Pillow\n",
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"# you are likely to get warning looks like WARNING:phonemizer:words count mismatch on 300.0% of the lines (3/1), this can be safely ignored"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "voicecraft",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.9.18"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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