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VoiceCraft
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Merge branch 'master' of https://github.com/chenxwh/VoiceCraft

This commit is contained in:
chenxwh 2024-04-19 10:46:05 +00:00
parent 9746a1f60c 4bd7b83b57
commit ef3dd8285b
3 changed files with 33 additions and 29 deletions
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1
.gitignore vendored
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@ -17,6 +17,7 @@ thumbs.db
*.mp3
*.pth
*.th
*.json
*durip*
*rtx*

1
gradio_requirements.txt
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@ -3,3 +3,4 @@ nltk>=3.8.1
openai-whisper>=20231117
aeneas>=1.7.3.0
whisperx>=3.1.1
huggingface_hub==0.22.2

58
inference_speech_editing.ipynb
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@ -32,6 +32,7 @@
"import torchaudio\n",
"import numpy as np\n",
"import random\n",
"from argparse import Namespace\n",
"\n",
"from data.tokenizer import (\n",
" AudioTokenizer,\n",
@ -84,6 +85,34 @@
" torch.backends.cudnn.deterministic = True\n",
"seed_everything(seed)\n",
"device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
"# load model, tokenizer, and other necessary files\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",
"# # 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",
"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) # will also put the neural codec model on gpu\n",
"\n",
"text_tokenizer = TextTokenizer(backend=\"espeak\")\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",
@ -199,40 +228,13 @@
"mask_interval = [[round(morphed_span[0]*codec_sr), round(morphed_span[1]*codec_sr)]]\n",
"mask_interval = torch.LongTensor(mask_interval) # [M,2], M==1 for now\n",
"\n",
"# load model, tokenizer, and other necessary files\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",
"# # 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",
"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) # will also put the neural codec model on gpu\n",
"\n",
"text_tokenizer = TextTokenizer(backend=\"espeak\")\n",
"\n",
"# run the model to get the output\n",
"from inference_speech_editing_scale import inference_one_sample\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}\n",
"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",
"orig_audio, new_audio = inference_one_sample(model, Namespace(**config), phn2num, text_tokenizer, audio_tokenizer, audio_fn, target_transcript, mask_interval, device, decode_config)\n",
" \n",
"# save segments for comparison\n",
"orig_audio, new_audio = orig_audio[0].cpu(), new_audio[0].cpu()\n",