From 741a6559e98c4299324ad6e9fd454fb48d8f3cae Mon Sep 17 00:00:00 2001 From: jason-on-salt-a40 Date: Sat, 30 Mar 2024 11:32:09 -0700 Subject: [PATCH] docker for inference, works on linux and windows --- .gitignore | 2 + README.md | 5 +- inference_speech_editing.ipynb | 3 +- inference_tts.ipynb | 132 ++++++++++++++++++--------------- 4 files changed, 80 insertions(+), 62 deletions(-) diff --git a/.gitignore b/.gitignore index 2647f93..17dbc9b 100644 --- a/.gitignore +++ b/.gitignore @@ -23,5 +23,7 @@ thumbs.db *l40* *a40* +src/audiocraft + !/demo/ !/demo/* \ No newline at end of file diff --git a/README.md b/README.md index a1dc4b3..3308a1d 100644 --- a/README.md +++ b/README.md @@ -11,6 +11,8 @@ To clone or edit an unseen voice, VoiceCraft needs only a few seconds of referen :star: 03/28/2024: Model weights are up on HuggingFace🤗 [here](https://huggingface.co/pyp1/VoiceCraft/tree/main)! ## QuickStart +:star: To try out TTS inference with VoiceCraft, the best way is using docker. Thank [@ubergarm](https://github.com/ubergarm) and [@jayc88](https://github.com/jay-c88) for making this happen. + Tested on Linux and Windows and should work with any host with docker installed. ```bash # 1. clone the repo on in a directory on a drive with plenty of free space @@ -38,11 +40,10 @@ sudo apt-get update nvidia-smi # 7. Now in browser, open inference_tts.ipynb and work through one cell at a time -echo GOOD LUCK AND BE NICE +echo GOOD LUCK ``` ## TODO -The TODOs left will be completed by the end of March 2024. - [x] Codebase upload - [x] Environment setup - [x] Inference demo for speech editing and TTS diff --git a/inference_speech_editing.ipynb b/inference_speech_editing.ipynb index 67f49cd..ddf199c 100644 --- a/inference_speech_editing.ipynb +++ b/inference_speech_editing.ipynb @@ -8,7 +8,8 @@ "source": [ "import os\n", "os.environ[\"CUDA_DEVICE_ORDER\"]=\"PCI_BUS_ID\" \n", - "os.environ[\"CUDA_VISIBLE_DEVICES\"]=\"7\"" + "os.environ[\"CUDA_VISIBLE_DEVICES\"]=\"7\"\n", + "os.environ[\"USER\"] = \"YOUR_USERNAME\" # TODO change this to your username" ] }, { diff --git a/inference_tts.ipynb b/inference_tts.ipynb index c8d5163..de18ce6 100644 --- a/inference_tts.ipynb +++ b/inference_tts.ipynb @@ -104,6 +104,7 @@ "import os\n", "os.environ[\"CUDA_DEVICE_ORDER\"]=\"PCI_BUS_ID\" \n", "os.environ[\"CUDA_VISIBLE_DEVICES\"]=\"0\"\n", + "os.environ[\"USER\"] = \"YOUR_USERNAME\" # TODO change this to your username\n", "\n", "import torch\n", "import torchaudio\n", @@ -120,56 +121,11 @@ "metadata": {}, "outputs": [], "source": [ - "# hyperparameters for inference\n", - "left_margin = 0.08 # not used for TTS, only for speech editing\n", - "right_margin = 0.08 # not used for TTS, only for speech editing\n", - "codec_audio_sr = 16000\n", - "codec_sr = 50\n", - "top_k = 0\n", - "top_p = 0.8\n", - "temperature = 1\n", - "kvcache = 1\n", - "silence_tokens=[1388,1898,131]\n", - "# adjust the below three arguments if the generation is not as good\n", - "seed = 1 # random seed magic\n", - "stop_repetition = 3 # if there are long silence in the generated audio, reduce the stop_repetition to 3, 2 or even 1\n", - "sample_batch_size = 4 # 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", - "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 = \"./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", - "os.makedirs(align_temp, exist_ok=True)\n", - "\n", - "# get into the conda environment and download the needed MFA models\n", + "# install MFA models and dictionaries if you haven't done so already\n", "!source ~/.bashrc && \\\n", " conda activate voicecraft && \\\n", " mfa model download dictionary english_us_arpa && \\\n", - " mfa model download acoustic english_us_arpa\n", - "\n", - "#os.system(f\". ~/.bashrc && conda activate voicecraft && mfa align -j 1 --output_format csv {temp_folder} english_us_arpa english_us_arpa {align_temp}\")\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", - "# 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\"" + " mfa model download acoustic english_us_arpa" ] }, { @@ -178,20 +134,12 @@ "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, 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", - "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, encodec, and phn2num\n", "# # load model, tokenizer, and other necessary files\n", + "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n", "from models import voicecraft\n", "#import models.voicecraft as voicecraft\n", - "voicecraft_name=\"giga830M.pth\"\n", + "voicecraft_name=\"giga830M.pth\" # or giga330M.pth\n", "ckpt_fn =f\"./pretrained_models/{voicecraft_name}\"\n", "encodec_fn = \"./pretrained_models/encodec_4cb2048_giga.th\"\n", "if not os.path.exists(ckpt_fn):\n", @@ -210,9 +158,75 @@ "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", + "audio_tokenizer = AudioTokenizer(signature=encodec_fn, device=device) # will also put the neural codec model on gpu\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "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 = 0.8\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 = 4 # 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", "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",