{ "nbformat": 4, "nbformat_minor": 0, "metadata": { "colab": { "provenance": [], "machine_shape": "hm", "gpuType": "T4", "authorship_tag": "ABX9TyN0+koRSo40PzlqWvbYetyv", "include_colab_link": true }, "kernelspec": { "name": "python3", "display_name": "Python 3" }, "language_info": { "name": "python" }, "accelerator": "GPU" }, "cells": [ { "cell_type": "markdown", "metadata": { "id": "view-in-github", "colab_type": "text" }, "source": [ "\"Open" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "LcqTi7OKxQge", "outputId": "4e019f41-aac9-4533-ccef-bbf89e277fcd" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Cloning into 'xtts'...\n", "remote: Enumerating objects: 376, done.\u001b[K\n", "remote: Counting objects: 100% (372/372), done.\u001b[K\n", "remote: Compressing objects: 100% (362/362), done.\u001b[K\n", "remote: Total 376 (delta 209), reused 0 (delta 0), pack-reused 4\u001b[K\n", "Receiving objects: 100% (376/376), 692.03 KiB | 3.76 MiB/s, done.\n", "Resolving deltas: 100% (209/209), done.\n", "Filtering content: 100% (2/2), 28.81 MiB | 5.39 MiB/s, done.\n" ] } ], "source": [ "!git clone https://huggingface.co/spaces/coqui/xtts" ] }, { "cell_type": "code", "source": [ "cd xtts" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "k6zSx_tmxSYF", "outputId": "d1743ea0-95a7-4ea4-f773-4629727801ec" }, "execution_count": 2, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "/content/xtts\n" ] } ] }, { "cell_type": "code", "source": [ "pip install -r requirements.txt" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000 }, "id": "KLVE5VYsxx3I", "outputId": "54fc0077-a37e-4d17-ecbb-dbfb9c991b0c" }, "execution_count": 3, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Ignoring numpy: markers 'python_version > \"3.10\"' don't match your environment\n", "Ignoring numba: markers 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nvidia-cuda-cupti-cu11, nvidia-cublas-cu11, numpy, num2words, networkx, llvmlite, jsonlines, inflect, gruut-ipa, fugashi, ftfy, einops, docker-pycreds, cython, coqpit, anyascii, nvidia-cusolver-cu11, nvidia-cudnn-cu11, numba, langid, huggingface-hub, gitdb, dateparser, cutlet, transformers, scikit-learn, gruut, GitPython, g2pkk, wandb, pynndescent, librosa, jsonmerge, umap-learn, triton, torch, torchvision, torchsde, torchdiffeq, torchaudio, kornia, clip-anytorch, clean-fid, accelerate, trainer, k_diffusion, encodec, TTS, deepspeed\n", " Attempting uninstall: tqdm\n", " Found existing installation: tqdm 4.66.1\n", " Uninstalling tqdm-4.66.1:\n", " Successfully uninstalled tqdm-4.66.1\n", " Attempting uninstall: packaging\n", " Found existing installation: packaging 23.2\n", " Uninstalling packaging-23.2:\n", " Successfully uninstalled packaging-23.2\n", " Attempting uninstall: numpy\n", " Found existing installation: numpy 1.23.5\n", " Uninstalling numpy-1.23.5:\n", " Successfully uninstalled numpy-1.23.5\n", " Attempting uninstall: networkx\n", " Found existing installation: networkx 3.2\n", " Uninstalling networkx-3.2:\n", " Successfully uninstalled networkx-3.2\n", " Attempting uninstall: llvmlite\n", " Found existing installation: llvmlite 0.39.1\n", " Uninstalling llvmlite-0.39.1:\n", " Successfully uninstalled llvmlite-0.39.1\n", " Attempting uninstall: inflect\n", " Found existing installation: inflect 7.0.0\n", " Uninstalling inflect-7.0.0:\n", " Successfully uninstalled inflect-7.0.0\n", " Attempting uninstall: cython\n", " Found existing installation: Cython 3.0.4\n", " Uninstalling Cython-3.0.4:\n", " Successfully uninstalled Cython-3.0.4\n", " Attempting uninstall: numba\n", " Found existing installation: numba 0.56.4\n", " Uninstalling numba-0.56.4:\n", " Successfully uninstalled numba-0.56.4\n", " Attempting uninstall: scikit-learn\n", " Found existing installation: scikit-learn 1.2.2\n", " Uninstalling scikit-learn-1.2.2:\n", " Successfully uninstalled scikit-learn-1.2.2\n", " Attempting uninstall: librosa\n", " Found existing installation: librosa 0.10.1\n", " Uninstalling librosa-0.10.1:\n", " Successfully uninstalled librosa-0.10.1\n", " Attempting uninstall: triton\n", " Found existing installation: triton 2.1.0\n", " Uninstalling triton-2.1.0:\n", " Successfully uninstalled triton-2.1.0\n", " Attempting uninstall: torch\n", " Found existing installation: torch 2.1.0+cu118\n", " Uninstalling torch-2.1.0+cu118:\n", " Successfully uninstalled torch-2.1.0+cu118\n", " Attempting uninstall: torchvision\n", " Found existing installation: torchvision 0.16.0+cu118\n", " Uninstalling torchvision-0.16.0+cu118:\n", " Successfully uninstalled torchvision-0.16.0+cu118\n", " Attempting uninstall: torchaudio\n", " Found existing installation: torchaudio 2.1.0+cu118\n", " Uninstalling torchaudio-2.1.0+cu118:\n", " Successfully uninstalled torchaudio-2.1.0+cu118\n", "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n", "lida 0.0.10 requires fastapi, which is not installed.\n", "lida 0.0.10 requires kaleido, which is not installed.\n", "lida 0.0.10 requires python-multipart, which is not installed.\n", "lida 0.0.10 requires uvicorn, which is not installed.\n", "plotnine 0.12.3 requires numpy>=1.23.0, but you have numpy 1.22.0 which is incompatible.\n", "tensorflow 2.14.0 requires numpy>=1.23.5, but you have numpy 1.22.0 which is incompatible.\n", "torchdata 0.7.0 requires torch==2.1.0, but you have torch 2.0.1 which is incompatible.\n", "torchtext 0.16.0 requires torch==2.1.0, but you have torch 2.0.1 which is incompatible.\u001b[0m\u001b[31m\n", "\u001b[0mSuccessfully installed GitPython-3.1.40 TTS-0.19.1 accelerate-0.24.1 anyascii-0.3.2 bangla-0.0.2 bnnumerizer-0.0.2 bnunicodenormalizer-0.1.6 clean-fid-0.1.35 clip-anytorch-2.5.2 coqpit-0.0.17 cutlet-0.3.0 cython-0.29.30 dateparser-1.1.8 deepspeed-0.8.3 docker-pycreds-0.4.0 docopt-0.6.2 einops-0.6.1 encodec-0.1.1 ftfy-6.1.1 fugashi-1.3.0 g2pkk-0.1.2 gitdb-4.0.11 gruut-2.2.3 gruut-ipa-0.13.0 gruut_lang_de-2.0.0 gruut_lang_en-2.0.0 gruut_lang_es-2.0.0 gruut_lang_fr-2.0.2 hjson-3.1.0 huggingface-hub-0.18.0 inflect-5.6.2 jaconv-0.3.4 jamo-0.4.1 jsonlines-1.2.0 jsonmerge-1.9.2 k_diffusion-0.0.16 kornia-0.7.0 langid-1.1.6 librosa-0.10.0 lit-17.0.4 llvmlite-0.40.1 mecab-python3-1.0.6 mojimoji-0.0.12 networkx-2.8.8 ninja-1.11.1.1 num2words-0.5.13 numba-0.57.0 numpy-1.22.0 nvidia-cublas-cu11-11.10.3.66 nvidia-cuda-cupti-cu11-11.7.101 nvidia-cuda-nvrtc-cu11-11.7.99 nvidia-cuda-runtime-cu11-11.7.99 nvidia-cudnn-cu11-8.5.0.96 nvidia-cufft-cu11-10.9.0.58 nvidia-curand-cu11-10.2.10.91 nvidia-cusolver-cu11-11.4.0.1 nvidia-cusparse-cu11-11.7.4.91 nvidia-nccl-cu11-2.14.3 nvidia-nvtx-cu11-11.7.91 packaging-23.1 pathtools-0.1.2 pydub-0.25.1 pynndescent-0.5.10 pypinyin-0.47.1 pysbd-0.3.4 python-crfsuite-0.9.9 resize-right-0.0.2 safetensors-0.4.0 scikit-learn-1.3.0 sentry-sdk-1.34.0 setproctitle-1.3.3 smmap-5.0.1 tokenizers-0.13.3 torch-2.0.1 torchaudio-2.0.2 torchdiffeq-0.2.3 torchsde-0.2.6 torchvision-0.15.2 tqdm-4.64.1 trainer-0.0.31 trampoline-0.1.2 transformers-4.33.3 triton-2.0.0 umap-learn-0.5.4 unidecode-1.3.7 unidic-lite-1.0.8 wandb-0.15.12\n" ] }, { "output_type": "display_data", "data": { "application/vnd.colab-display-data+json": { "pip_warning": { "packages": [ "numpy" ] } } }, "metadata": {} } ] }, { "cell_type": "code", "source": [ "pip install gradio==3.42.0" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "7o0DgoYC10eC", "outputId": "9873b834-dd28-495a-d00a-9f9d8ef1ce3c" }, "execution_count": 4, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Collecting gradio==3.42.0\n", " Downloading gradio-3.42.0-py3-none-any.whl (20.1 MB)\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m20.1/20.1 MB\u001b[0m \u001b[31m68.9 MB/s\u001b[0m eta 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numpy~=1.0 in /usr/local/lib/python3.10/dist-packages (from gradio==3.42.0) (1.22.0)\n", "Collecting orjson~=3.0 (from gradio==3.42.0)\n", " Downloading orjson-3.9.10-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (138 kB)\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m138.7/138.7 kB\u001b[0m \u001b[31m16.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[?25hRequirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from gradio==3.42.0) (23.1)\n", "Requirement already satisfied: pandas<3.0,>=1.0 in /usr/local/lib/python3.10/dist-packages (from gradio==3.42.0) (1.5.3)\n", "Requirement already satisfied: pillow<11.0,>=8.0 in /usr/local/lib/python3.10/dist-packages (from gradio==3.42.0) (9.4.0)\n", "Requirement already satisfied: pydantic!=1.8,!=1.8.1,!=2.0.0,!=2.0.1,<3.0.0,>=1.7.4 in /usr/local/lib/python3.10/dist-packages (from gradio==3.42.0) (1.10.13)\n", "Requirement already satisfied: pydub in 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already satisfied: toolz in /usr/local/lib/python3.10/dist-packages (from altair<6.0,>=4.2.0->gradio==3.42.0) (0.12.0)\n", "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from huggingface-hub>=0.14.0->gradio==3.42.0) (3.12.4)\n", "Requirement already satisfied: tqdm>=4.42.1 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub>=0.14.0->gradio==3.42.0) (4.64.1)\n", "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib~=3.0->gradio==3.42.0) (1.1.1)\n", "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.10/dist-packages (from matplotlib~=3.0->gradio==3.42.0) (0.12.1)\n", "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib~=3.0->gradio==3.42.0) (4.43.1)\n", "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib~=3.0->gradio==3.42.0) (1.4.5)\n", "Requirement 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"Requirement already satisfied: click>=7.0 in /usr/local/lib/python3.10/dist-packages (from uvicorn>=0.14.0->gradio==3.42.0) (8.1.7)\n", "Collecting h11>=0.8 (from uvicorn>=0.14.0->gradio==3.42.0)\n", " Downloading h11-0.14.0-py3-none-any.whl (58 kB)\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m58.3/58.3 kB\u001b[0m \u001b[31m7.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[?25hRequirement already satisfied: anyio<4.0.0,>=3.7.1 in /usr/local/lib/python3.10/dist-packages (from fastapi->gradio==3.42.0) (3.7.1)\n", "Collecting starlette<0.28.0,>=0.27.0 (from fastapi->gradio==3.42.0)\n", " Downloading starlette-0.27.0-py3-none-any.whl (66 kB)\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m67.0/67.0 kB\u001b[0m \u001b[31m9.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[?25hCollecting typing-extensions~=4.0 (from gradio==3.42.0)\n", " Using cached typing_extensions-4.8.0-py3-none-any.whl (31 kB)\n", "Collecting httpcore (from httpx->gradio==3.42.0)\n", " Downloading httpcore-1.0.1-py3-none-any.whl (76 kB)\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m76.9/76.9 kB\u001b[0m \u001b[31m11.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[?25hRequirement already satisfied: sniffio in /usr/local/lib/python3.10/dist-packages (from httpx->gradio==3.42.0) (1.3.0)\n", "Requirement already satisfied: exceptiongroup in /usr/local/lib/python3.10/dist-packages (from anyio<4.0.0,>=3.7.1->fastapi->gradio==3.42.0) (1.1.3)\n", "Requirement already satisfied: attrs>=22.2.0 in /usr/local/lib/python3.10/dist-packages (from jsonschema>=3.0->altair<6.0,>=4.2.0->gradio==3.42.0) (23.1.0)\n", "Requirement already satisfied: jsonschema-specifications>=2023.03.6 in /usr/local/lib/python3.10/dist-packages (from jsonschema>=3.0->altair<6.0,>=4.2.0->gradio==3.42.0) (2023.7.1)\n", "Requirement already satisfied: referencing>=0.28.4 in /usr/local/lib/python3.10/dist-packages (from jsonschema>=3.0->altair<6.0,>=4.2.0->gradio==3.42.0) (0.30.2)\n", "Requirement already satisfied: rpds-py>=0.7.1 in /usr/local/lib/python3.10/dist-packages (from jsonschema>=3.0->altair<6.0,>=4.2.0->gradio==3.42.0) (0.10.6)\n", "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.7->matplotlib~=3.0->gradio==3.42.0) (1.16.0)\n", "Building wheels for collected packages: ffmpy\n", " Building wheel for ffmpy (setup.py) ... \u001b[?25l\u001b[?25hdone\n", " Created wheel for ffmpy: filename=ffmpy-0.3.1-py3-none-any.whl size=5579 sha256=62722285dc99de7893509b08b63757672ec27a432640ee9fc32e6d08f555d9c1\n", " Stored in directory: /root/.cache/pip/wheels/01/a6/d1/1c0828c304a4283b2c1639a09ad86f83d7c487ef34c6b4a1bf\n", "Successfully built ffmpy\n", "Installing collected packages: ffmpy, websockets, typing-extensions, semantic-version, python-multipart, orjson, h11, aiofiles, uvicorn, starlette, httpcore, httpx, fastapi, gradio-client, gradio\n", " Attempting uninstall: typing-extensions\n", " Found existing installation: typing_extensions 4.5.0\n", " Uninstalling typing_extensions-4.5.0:\n", " Successfully uninstalled typing_extensions-4.5.0\n", "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n", "lida 0.0.10 requires kaleido, which is not installed.\n", "tensorflow 2.14.0 requires numpy>=1.23.5, but you have numpy 1.22.0 which is incompatible.\n", "tensorflow-probability 0.22.0 requires typing-extensions<4.6.0, but you have typing-extensions 4.8.0 which is incompatible.\n", "torchdata 0.7.0 requires torch==2.1.0, but you have torch 2.0.1 which is incompatible.\n", "torchtext 0.16.0 requires torch==2.1.0, but you have torch 2.0.1 which is incompatible.\u001b[0m\u001b[31m\n", "\u001b[0mSuccessfully installed aiofiles-23.2.1 fastapi-0.104.1 ffmpy-0.3.1 gradio-3.42.0 gradio-client-0.5.0 h11-0.14.0 httpcore-1.0.1 httpx-0.25.1 orjson-3.9.10 python-multipart-0.0.6 semantic-version-2.10.0 starlette-0.27.0 typing-extensions-4.8.0 uvicorn-0.23.2 websockets-11.0.3\n" ] } ] }, { "cell_type": "code", "source": [ "import gradio as gr" ], "metadata": { "id": "oHSHVqr_x13Q" }, "execution_count": 5, "outputs": [] }, { "cell_type": "code", "source": [ "import sys\n", "import io, os, stat\n", "import subprocess\n", "import random\n", "from zipfile import ZipFile\n", "import uuid\n", "import time\n", "import torch\n", "import torchaudio\n", "# By using XTTS you agree to CPML license https://coqui.ai/cpml\n", "os.environ[\"COQUI_TOS_AGREED\"] = \"1\"\n", "\n", "# langid is used to detect language for longer text\n", "# Most users expect text to be their own language, there is checkbox to disable it\n", "import langid\n", "import base64\n", "import csv\n", "from io import StringIO\n", "import datetime\n", "\n", "import gradio as gr\n", "from scipy.io.wavfile import write\n", "from pydub import AudioSegment\n", "\n", "from TTS.api import TTS\n", "from TTS.tts.configs.xtts_config import XttsConfig\n", "from TTS.tts.models.xtts import Xtts\n", "from TTS.utils.generic_utils import get_user_data_dir\n", "\n", "HF_TOKEN = os.environ.get(\"HF_TOKEN\")\n", "\n", "from huggingface_hub import HfApi\n", "\n", "# will use api to restart space on a unrecoverable error\n", "api = HfApi(token=HF_TOKEN)\n", "repo_id = \"coqui/xtts\"\n", "\n", "# Use never ffmpeg binary for Ubuntu20 to use denoising for microphone input\n", "print(\"Export newer ffmpeg binary for denoise filter\")\n", "ZipFile(\"ffmpeg.zip\").extractall()\n", "print(\"Make ffmpeg binary executable\")\n", "st = os.stat('ffmpeg')\n", "os.chmod('ffmpeg', st.st_mode | stat.S_IEXEC)\n", "\n", "# This will trigger downloading model\n", "print(\"Downloading if not downloaded Coqui XTTS V1.1\")\n", "from TTS.utils.manage import ModelManager\n", "model_name = \"tts_models/multilingual/multi-dataset/xtts_v1.1\"\n", "ModelManager().download_model(model_name)\n", "model_path = os.path.join(get_user_data_dir(\"tts\"), model_name.replace(\"/\", \"--\"))\n", "print(\"XTTS downloaded\")\n", "\n", "config = XttsConfig()\n", "config.load_json(os.path.join(model_path, \"config.json\"))\n", "\n", "# it should be there just to be sure\n", "if \"ja\" not in config.languages:\n", " config.languages.append(\"ja\")\n", "\n", "model = Xtts.init_from_config(config)\n", "model.load_checkpoint(\n", " config,\n", " checkpoint_path=os.path.join(model_path, \"model.pth\"),\n", " vocab_path=os.path.join(model_path, \"vocab.json\"),\n", " eval=True,\n", " use_deepspeed=True\n", ")\n", "model.cuda()\n", "\n", "# This is for debugging purposes only\n", "DEVICE_ASSERT_DETECTED=0\n", "DEVICE_ASSERT_PROMPT=None\n", "DEVICE_ASSERT_LANG=None\n", "\n", "\n", "\n", "#supported_languages=[\"en\",\"es\",\"fr\",\"de\",\"it\",\"pt\",\"pl\",\"tr\",\"ru\",\"nl\",\"cs\",\"ar\",\"zh-cn\"]\n", "supported_languages=config.languages\n", "\n", "def predict(prompt, language, audio_file_pth, mic_file_path, use_mic, voice_cleanup, no_lang_auto_detect, agree,):\n", " if agree == True:\n", "\n", "\n", " if language not in supported_languages:\n", " gr.Warning(f\"Language you put {language} in is not in is not in our Supported Languages, please choose from dropdown\")\n", "\n", " return (\n", " None,\n", " None,\n", " None,\n", " None,\n", " )\n", "\n", " language_predicted=langid.classify(prompt)[0].strip() # strip need as there is space at end!\n", "\n", " # tts expects chinese as zh-cn\n", " if language_predicted == \"zh\":\n", " #we use zh-cn\n", " language_predicted = \"zh-cn\"\n", "\n", " print(f\"Detected language:{language_predicted}, Chosen language:{language}\")\n", "\n", " # After text character length 15 trigger language detection\n", " if len(prompt)>15:\n", " # allow any language for short text as some may be common\n", " # If user unchecks language autodetection it will not trigger\n", " # You may remove this completely for own use\n", " if language_predicted != language and not no_lang_auto_detect:\n", " #Please duplicate and remove this check if you really want this\n", " #Or auto-detector fails to identify language (which it can on pretty short text or mixed text)\n", " gr.Warning(f\"It looks like your text isn’t the language you chose , if you’re sure the text is the same language you chose, please check disable language auto-detection checkbox\" )\n", "\n", " return (\n", " None,\n", " None,\n", " None,\n", " None,\n", " )\n", "\n", "\n", " if use_mic == True:\n", " if mic_file_path is not None:\n", " speaker_wav=mic_file_path\n", " else:\n", " gr.Warning(\"Please record your voice with Microphone, or uncheck Use Microphone to use reference audios\")\n", " return (\n", " None,\n", " None,\n", " None,\n", " None,\n", " )\n", "\n", " else:\n", " speaker_wav=audio_file_pth\n", "\n", "\n", " # Filtering for microphone input, as it has BG noise, maybe silence in beginning and end\n", " # This is fast filtering not perfect\n", "\n", " # Apply all on demand\n", " lowpassfilter=denoise=trim=loudness=True\n", "\n", " if lowpassfilter:\n", " lowpass_highpass=\"lowpass=8000,highpass=75,\"\n", " else:\n", " lowpass_highpass=\"\"\n", "\n", " if trim:\n", " # better to remove silence in beginning and end for microphone\n", " trim_silence=\"areverse,silenceremove=start_periods=1:start_silence=0:start_threshold=0.02,areverse,silenceremove=start_periods=1:start_silence=0:start_threshold=0.02,\"\n", " else:\n", " trim_silence=\"\"\n", "\n", " if (voice_cleanup):\n", " try:\n", " out_filename = speaker_wav + str(uuid.uuid4()) + \".wav\" #ffmpeg to know output format\n", "\n", " #we will use newer ffmpeg as that has afftn denoise filter\n", " shell_command = f\"./ffmpeg -y -i {speaker_wav} -af {lowpass_highpass}{trim_silence} {out_filename}\".split(\" \")\n", "\n", " command_result = subprocess.run([item for item in shell_command], capture_output=False,text=True, check=True)\n", " speaker_wav=out_filename\n", " print(\"Filtered microphone input\")\n", " except subprocess.CalledProcessError:\n", " # There was an error - command exited with non-zero code\n", " print(\"Error: failed filtering, use original microphone input\")\n", " else:\n", " speaker_wav=speaker_wav\n", "\n", " if len(prompt)<2:\n", " gr.Warning(\"Please give a longer prompt text\")\n", " return (\n", " None,\n", " None,\n", " None,\n", " None,\n", " )\n", " if len(prompt)>1000:\n", " gr.Warning(\"Text length limited to 1000 characters for this demo, please try shorter text. You can clone this space and edit code for your own usage\")\n", " return (\n", " None,\n", " None,\n", " None,\n", " None,\n", " )\n", " global DEVICE_ASSERT_DETECTED\n", " if DEVICE_ASSERT_DETECTED:\n", " global DEVICE_ASSERT_PROMPT\n", " global DEVICE_ASSERT_LANG\n", " #It will likely never come here as we restart space on first unrecoverable error now\n", " print(f\"Unrecoverable exception caused by language:{DEVICE_ASSERT_LANG} prompt:{DEVICE_ASSERT_PROMPT}\")\n", "\n", " try:\n", " metrics_text=\"\"\n", " t_latent=time.time()\n", "\n", " # note diffusion_conditioning not used on hifigan (default mode), it will be empty but need to pass it to model.inference\n", " try:\n", " gpt_cond_latent, diffusion_conditioning, speaker_embedding = model.get_conditioning_latents(audio_path=speaker_wav)\n", " except Exception as e:\n", " print(\"Speaker encoding error\", str(e))\n", " gr.Warning(\"It appears something wrong with reference, did you unmute your microphone?\")\n", " return (\n", " None,\n", " None,\n", " None,\n", " None,\n", " )\n", "\n", "\n", " latent_calculation_time = time.time() - t_latent\n", " #metrics_text=f\"Embedding calculation time: {latent_calculation_time:.2f} seconds\\n\"\n", "\n", " wav_chunks = []\n", "\n", " print(\"I: Generating new audio...\")\n", " t0 = time.time()\n", " out = model.inference(\n", " prompt,\n", " language,\n", " gpt_cond_latent,\n", " speaker_embedding,\n", " diffusion_conditioning,\n", " decoder=\"ne_hifigan\",\n", " )\n", " inference_time = time.time() - t0\n", " print(f\"I: Time to generate audio: {round(inference_time*1000)} milliseconds\")\n", " metrics_text+=f\"Time to generate audio: {round(inference_time*1000)} milliseconds\\n\"\n", " real_time_factor= (time.time() - t0) / out['wav'].shape[-1] * 24000\n", " print(f\"Real-time factor (RTF): {real_time_factor}\")\n", " metrics_text+=f\"Real-time factor (RTF): {real_time_factor:.2f}\\n\"\n", " torchaudio.save(\"output.wav\", torch.tensor(out[\"wav\"]).unsqueeze(0), 24000)\n", " except RuntimeError as e :\n", " if \"device-side assert\" in str(e):\n", " # cannot do anything on cuda device side error, need tor estart\n", " print(f\"Exit due to: Unrecoverable exception caused by language:{language} prompt:{prompt}\", flush=True)\n", " gr.Warning(\"Unhandled Exception encounter, please retry in a minute\")\n", " print(\"Cuda device-assert Runtime encountered need restart\")\n", " if not DEVICE_ASSERT_DETECTED:\n", " DEVICE_ASSERT_DETECTED=1\n", " DEVICE_ASSERT_PROMPT=prompt\n", " DEVICE_ASSERT_LANG=language\n", "\n", " # just before restarting save what caused the issue so we can handle it in future\n", " # Uploading Error data only happens for unrecovarable error\n", " error_time = datetime.datetime.now().strftime('%d-%m-%Y-%H:%M:%S')\n", " error_data = [error_time, prompt, language, audio_file_pth, mic_file_path, use_mic, voice_cleanup, no_lang_auto_detect, agree]\n", " error_data = [str(e) if type(e)!=str else e for e in error_data]\n", " print(error_data)\n", " print(speaker_wav)\n", " write_io = StringIO()\n", " csv.writer(write_io).writerows([error_data])\n", " csv_upload= write_io.getvalue().encode()\n", "\n", " filename = error_time+\"_\" + str(uuid.uuid4()) +\".csv\"\n", " print(\"Writing error csv\")\n", " error_api = HfApi()\n", " error_api.upload_file(\n", " path_or_fileobj=csv_upload,\n", " path_in_repo=filename,\n", " repo_id=\"coqui/xtts-flagged-dataset\",\n", " repo_type=\"dataset\",\n", " )\n", "\n", " #speaker_wav\n", " print(\"Writing error reference audio\")\n", " speaker_filename = error_time+\"_reference_\"+ str(uuid.uuid4()) +\".wav\"\n", " error_api = HfApi()\n", " error_api.upload_file(\n", " path_or_fileobj=speaker_wav,\n", " path_in_repo=speaker_filename,\n", " repo_id=\"coqui/xtts-flagged-dataset\",\n", " repo_type=\"dataset\",\n", " )\n", "\n", " # HF Space specific.. This error is unrecoverable need to restart space\n", " api.restart_space(repo_id=repo_id)\n", " else:\n", " if \"Failed to decode\" in str(e):\n", " print(\"Speaker encoding error\", str(e))\n", " gr.Warning(\"It appears something wrong with reference, did you unmute your microphone?\")\n", " else:\n", " print(\"RuntimeError: non device-side assert error:\", str(e))\n", " gr.Warning(\"Something unexpected happened please retry again.\")\n", " return (\n", " None,\n", " None,\n", " None,\n", " None,\n", " )\n", " return (\n", " gr.make_waveform(\n", " audio=\"output.wav\",\n", " ),\n", " \"output.wav\",\n", " metrics_text,\n", " speaker_wav,\n", " )\n", " else:\n", " gr.Warning(\"Please accept the Terms & Condition!\")\n", " return (\n", " None,\n", " None,\n", " None,\n", " None,\n", " )\n", "\n", "\n", "title = \"🐸🌊💕 XTTS — 只需3秒语音，一键复刻14种语言\"\n", "\n", "description = \"\"\"\n", "##
🌟 - 3秒语音开启真实拟声，支持中日英在内的14种语言！Powered by [Coqui AI](https://coqui.ai/)
\n", "###
🌊 - 更多精彩应用，敬请关注[滔滔AI](http://www.talktalkai.com)；滔滔AI，为爱滔滔！💕
\n", "\"\"\"\n", "\n", "article = \"\"\"\n", "

Language Selectors:\n", "Arabic: ar, Brazilian Portuguese: pt , Chinese: zh-cn, Czech: cs,
\n", "Dutch: nl, English: en, French: fr, Italian: it, Polish: pl,
\n", "Russian: ru, Spanish: es, Turkish: tr, Japanese: ja
\n", "

\n", "###
注意❗：请不要生成会对个人以及组织造成侵害的内容，此程序仅供科研、学习及个人娱乐使用。
\n", "
\n", "

🌊🏞️🎶 - 江水东流急，滔滔无尽声。 明·顾璘\n", "

\n", "
\n", "\"\"\"\n", "examples = [\n", " [\n", " \"Once when I was six years old I saw a magnificent picture\",\n", " \"en\",\n", " \"examples/female.wav\",\n", " None,\n", " False,\n", " False,\n", " False,\n", " True,\n", "\n", " ],\n", " [\n", " \"Lorsque j'avais six ans j'ai vu, une fois, une magnifique image\",\n", " \"fr\",\n", " \"examples/male.wav\",\n", " None,\n", " False,\n", " False,\n", " False,\n", " True,\n", " ],\n", " [\n", " \"Als ich sechs war, sah ich einmal ein wunderbares Bild\",\n", " \"de\",\n", " \"examples/female.wav\",\n", " None,\n", " False,\n", " False,\n", " False,\n", " True,\n", " ],\n", " [\n", " \"Cuando tenía seis años, vi una vez una imagen magnífica\",\n", " \"es\",\n", " \"examples/male.wav\",\n", " None,\n", " False,\n", " False,\n", " False,\n", " True,\n", " ],\n", " [\n", " \"Quando eu tinha seis anos eu vi, uma vez, uma imagem magnífica\",\n", " \"pt\",\n", " \"examples/female.wav\",\n", " None,\n", " False,\n", " False,\n", " False,\n", " True,\n", " ],\n", " [\n", " \"Kiedy miałem sześć lat, zobaczyłem pewnego razu wspaniały obrazek\",\n", " \"pl\",\n", " \"examples/male.wav\",\n", " None,\n", " False,\n", " False,\n", " False,\n", " True,\n", " ],\n", " [\n", " \"Un tempo lontano, quando avevo sei anni, vidi un magnifico disegno\",\n", " \"it\",\n", " \"examples/female.wav\",\n", " None,\n", " False,\n", " False,\n", " False,\n", " True,\n", " ],\n", " [\n", " \"Bir zamanlar, altı yaşındayken, muhteşem bir resim gördüm\",\n", " \"tr\",\n", " \"examples/female.wav\",\n", " None,\n", " False,\n", " False,\n", " False,\n", " True,\n", " ],\n", " [\n", " \"Когда мне было шесть лет, я увидел однажды удивительную картинку\",\n", " \"ru\",\n", " \"examples/female.wav\",\n", " None,\n", " False,\n", " False,\n", " False,\n", " True,\n", " ],\n", " [\n", " \"Toen ik een jaar of zes was, zag ik op een keer een prachtige plaat\",\n", " \"nl\",\n", " \"examples/male.wav\",\n", " None,\n", " False,\n", " False,\n", " False,\n", " True,\n", " ],\n", " [\n", " \"Když mi bylo šest let, viděl jsem jednou nádherný obrázek\",\n", " \"cs\",\n", " \"examples/female.wav\",\n", " None,\n", " False,\n", " False,\n", " False,\n", " True,\n", " ],\n", " [\n", " \"当我还只有六岁的时候， 看到了一副精彩的插画\",\n", " \"zh-cn\",\n", " \"examples/female.wav\",\n", " None,\n", " False,\n", " False,\n", " False,\n", " True,\n", " ],\n", " [\n", " \"かつて 六歳のとき、素晴らしい絵を見ました\",\n", " \"ja\",\n", " \"examples/female.wav\",\n", " None,\n", " False,\n", " True,\n", " False,\n", " True,\n", " ],\n", "]\n", "\n", "\n", "\n", "gr.Interface(\n", " fn=predict,\n", " inputs=[\n", " gr.Textbox(\n", " label=\"请填写您想复刻的内容\",\n", " placeholder=\"想说却还没说的 还很多 攒着是因为想写成歌\",\n", " ),\n", " gr.Dropdown(\n", " label=\"请选择复刻内容对应的语言\",\n", " choices=[\n", " \"en\",\n", " \"es\",\n", " \"fr\",\n", " \"de\",\n", " \"it\",\n", " \"pt\",\n", " \"pl\",\n", " \"tr\",\n", " \"ru\",\n", " \"nl\",\n", " \"cs\",\n", " \"ar\",\n", " \"zh-cn\",\n", " \"ja\"\n", " ],\n", " max_choices=1,\n", " value=\"zh-cn\",\n", " ),\n", " gr.Audio(\n", " label=\"方法一：请上传您喜欢的语音文件\",\n", " type=\"filepath\",\n", " value=\"examples/female.wav\",\n", " ),\n", " gr.Audio(source=\"microphone\",\n", " type=\"filepath\",\n", " label=\"方法二：请用麦克风上传您喜欢的声音\"),\n", " gr.Checkbox(label=\"是否使用麦克风上传声音（默认为否）\",\n", " value=False,\n", " ),\n", " gr.Checkbox(label=\"是否对上传的语音进行降噪处理（默认为否）\",\n", " value=False,\n", " info=\"如果您上传的语音有明显噪声，请选择降噪\",\n", " ),\n", " gr.Checkbox(label=\"Do not use language auto-detect\",\n", " value=False,\n", " visible=False,\n", " info=\"Check to disable language auto-detection\",),\n", " gr.Checkbox(\n", " label=\"Agree\",\n", " value=True,\n", " visible=False,\n", " info=\"I agree to the terms of the Coqui Public Model License at https://coqui.ai/cpml\",\n", " ),\n", "\n", "\n", " ],\n", " outputs=[\n", " gr.Video(label=\"专属语音的波形图\"),\n", " gr.Audio(label=\"为您合成的专属语音\",autoplay=True),\n", " gr.Text(label=\"Metrics\", visible=False),\n", " gr.Audio(label=\"Reference Audio Used\", visible=False),\n", " ],\n", " title=title,\n", " description=description,\n", " article=article,\n", " examples=examples,\n", ").queue().launch(debug=True,show_api=True)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000 }, "id": "tcRpBSQz1yIz", "outputId": "2a922c0f-19a6-4b43-dcf3-0d357ad5b1ef" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Export newer ffmpeg binary for denoise filter\n", "Make ffmpeg binary executable\n", "Downloading if not downloaded Coqui XTTS V1.1\n", " > Downloading model to /root/.local/share/tts/tts_models--multilingual--multi-dataset--xtts_v1.1\n", " > Model's license - CPML\n", " > Check https://coqui.ai/cpml.txt for more info.\n", "XTTS downloaded\n", "[2023-11-04 06:40:58,748] [INFO] [logging.py:93:log_dist] [Rank -1] DeepSpeed info: version=0.8.3, git-hash=unknown, git-branch=unknown\n", "[2023-11-04 06:40:58,752] [WARNING] [config_utils.py:75:_process_deprecated_field] Config parameter replace_method is deprecated. This parameter is no longer needed, please remove from your call to DeepSpeed-inference\n", "[2023-11-04 06:40:58,754] [WARNING] [config_utils.py:75:_process_deprecated_field] Config parameter mp_size is deprecated use tensor_parallel.tp_size instead\n", "[2023-11-04 06:40:58,756] [INFO] [logging.py:93:log_dist] [Rank -1] quantize_bits = 8 mlp_extra_grouping = False, quantize_groups = 1\n", "Installed CUDA version 11.8 does not match the version torch was compiled with 11.7 but since the APIs are compatible, accepting this combination\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "Using /root/.cache/torch_extensions/py310_cu117 as PyTorch extensions root...\n", "Creating extension directory /root/.cache/torch_extensions/py310_cu117/transformer_inference...\n", "Detected CUDA files, patching ldflags\n", "Emitting ninja build file /root/.cache/torch_extensions/py310_cu117/transformer_inference/build.ninja...\n", "Building extension module transformer_inference...\n", "Allowing ninja to set a default number of workers... (overridable by setting the environment variable MAX_JOBS=N)\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "Time to load transformer_inference op: 35.26722860336304 seconds\n", "[2023-11-04 06:41:35,670] [INFO] [logging.py:93:log_dist] [Rank -1] DeepSpeed-Inference config: {'layer_id': 0, 'hidden_size': 1024, 'intermediate_size': 4096, 'heads': 16, 'num_hidden_layers': -1, 'fp16': False, 'pre_layer_norm': True, 'local_rank': -1, 'stochastic_mode': False, 'epsilon': 1e-05, 'mp_size': 1, 'q_int8': False, 'scale_attention': True, 'triangular_masking': True, 'local_attention': False, 'window_size': 1, 'rotary_dim': -1, 'rotate_half': False, 'rotate_every_two': True, 'return_tuple': True, 'mlp_after_attn': True, 'mlp_act_func_type': , 'specialized_mode': False, 'training_mp_size': 1, 'bigscience_bloom': False, 'max_out_tokens': 1024, 'scale_attn_by_inverse_layer_idx': False, 'enable_qkv_quantization': False, 'use_mup': False, 'return_single_tuple': False}\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "Loading extension module transformer_inference...\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "Installed CUDA version 11.8 does not match the version torch was compiled with 11.7 but since the APIs are compatible, accepting this combination\n", "Time to load transformer_inference op: 0.003851175308227539 seconds\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "Using /root/.cache/torch_extensions/py310_cu117 as PyTorch extensions root...\n", "No modifications detected for re-loaded extension module transformer_inference, skipping build step...\n", "Loading extension module transformer_inference...\n", "/usr/local/lib/python3.10/dist-packages/gradio/components/dropdown.py:95: UserWarning: The `max_choices` parameter is ignored when `multiselect` is False.\n", " warnings.warn(\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "Setting queue=True in a Colab notebook requires sharing enabled. Setting `share=True` (you can turn this off by setting `share=False` in `launch()` explicitly).\n", "\n", "Colab notebook detected. This cell will run indefinitely so that you can see errors and logs. To turn off, set debug=False in launch().\n", "Running on public URL: https://2deaddf58bf5be13bb.gradio.live\n", "\n", "This share link expires in 72 hours. For free permanent hosting and GPU upgrades, run `gradio deploy` from Terminal to deploy to Spaces (https://huggingface.co/spaces)\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "" ], "text/html": [ "
" ] }, "metadata": {} }, { "output_type": "stream", "name": "stdout", "text": [ "Detected language:en, Chosen language:en\n", "I: Generating new audio...\n", "I: Time to generate audio: 1719 milliseconds\n", "Real-time factor (RTF): 0.4464976510182642\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "/usr/local/lib/python3.10/dist-packages/matplotlib/axes/_axes.py:2229: RuntimeWarning: overflow encountered in int_scalars\n", " dx = [convert(x0 + ddx) - x for ddx in dx]\n", "/usr/local/lib/python3.10/dist-packages/matplotlib/patches.py:739: RuntimeWarning: overflow encountered in int_scalars\n", " y1 = self.convert_yunits(self._y0 + self._height)\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "Detected language:en, Chosen language:en\n", "I: Generating new audio...\n", "I: Time to generate audio: 883 milliseconds\n", "Real-time factor (RTF): 0.23196543715581172\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "/usr/local/lib/python3.10/dist-packages/matplotlib/axes/_axes.py:2229: RuntimeWarning: overflow encountered in int_scalars\n", " dx = [convert(x0 + ddx) - x for ddx in dx]\n", "/usr/local/lib/python3.10/dist-packages/matplotlib/patches.py:739: RuntimeWarning: overflow encountered in int_scalars\n", " y1 = self.convert_yunits(self._y0 + self._height)\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "Detected language:en, Chosen language:en\n", "I: Generating new audio...\n", "I: Time to generate audio: 4109 milliseconds\n", "Real-time factor (RTF): 0.2263596874566532\n", "Detected language:en, Chosen language:en\n", "I: Generating new audio...\n", "I: Time to generate audio: 2806 milliseconds\n", "Real-time factor (RTF): 0.22899703786516315\n", "Detected language:en, Chosen language:en\n", "I: Generating new audio...\n", "I: Time to generate audio: 3593 milliseconds\n", "Real-time factor (RTF): 0.23168362830823194\n", "Detected language:en, Chosen language:en\n", "Error: failed filtering, use original microphone input\n", "I: Generating new audio...\n", "I: Time to generate audio: 3597 milliseconds\n", "Real-time factor (RTF): 0.23196151856707084\n", "Detected language:en, Chosen language:en\n", "Error: failed filtering, use original microphone input\n", "I: Generating new audio...\n", "I: Time to generate audio: 4133 milliseconds\n", "Real-time factor (RTF): 0.22763659965277558\n", "Detected language:en, Chosen language:en\n", "Error: failed filtering, use original microphone input\n", "I: Generating new audio...\n", "I: Time to generate audio: 4128 milliseconds\n", "Real-time factor (RTF): 0.22740595121501334\n", "Detected language:en, Chosen language:en\n", "I: Generating new audio...\n", "I: Time to generate audio: 1671 milliseconds\n", "Real-time factor (RTF): 0.23517391837394988\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "/usr/local/lib/python3.10/dist-packages/matplotlib/axes/_axes.py:2229: RuntimeWarning: overflow encountered in int_scalars\n", " dx = [convert(x0 + ddx) - x for ddx in dx]\n", "/usr/local/lib/python3.10/dist-packages/matplotlib/patches.py:739: RuntimeWarning: overflow encountered in int_scalars\n", " y1 = self.convert_yunits(self._y0 + self._height)\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "Detected language:en, Chosen language:en\n", "I: Generating new audio...\n", "I: Time to generate audio: 2370 milliseconds\n", "Real-time factor (RTF): 0.23003857328284602\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "/usr/local/lib/python3.10/dist-packages/matplotlib/axes/_axes.py:2229: RuntimeWarning: overflow encountered in int_scalars\n", " dx = [convert(x0 + ddx) - x for ddx in dx]\n", "/usr/local/lib/python3.10/dist-packages/matplotlib/patches.py:739: RuntimeWarning: overflow encountered in int_scalars\n", " y1 = self.convert_yunits(self._y0 + self._height)\n" ] } ] }, { "cell_type": "code", "source": [], "metadata": { "id": "pekI9WQJCH-u" }, "execution_count": null, "outputs": [] } ] }