From 2b2406cd44c9e01585c40b03494a447b876d90d7 Mon Sep 17 00:00:00 2001 From: YeonwooSung Date: Fri, 22 Mar 2024 22:12:26 +0900 Subject: [PATCH] feat: Add notebook for TTS finetuning --- MultiModal/tts/tts-text-to-speech.ipynb | 7927 +++++++++++++++++++++++ 1 file changed, 7927 insertions(+) create mode 100644 MultiModal/tts/tts-text-to-speech.ipynb diff --git a/MultiModal/tts/tts-text-to-speech.ipynb b/MultiModal/tts/tts-text-to-speech.ipynb new file mode 100644 index 0000000..a6ea0c7 --- /dev/null +++ b/MultiModal/tts/tts-text-to-speech.ipynb @@ -0,0 +1,7927 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "c316c80b", + "metadata": { + "id": "ySBqaIDK-Jrw", + "papermill": { + "duration": 0.045744, + "end_time": "2024-01-22T19:03:31.194147", + "exception": false, + "start_time": "2024-01-22T19:03:31.148403", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "
\n", + "

Text to Speech (TTS)

\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "4cb4fe42", + "metadata": { + "papermill": { + "duration": 0.04559, + "end_time": "2024-01-22T19:03:31.286099", + "exception": false, + "start_time": "2024-01-22T19:03:31.240509", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "# Introduction\n", + "\n", + "📌 **The objective of this notebook is to compare different Text-to-Speech (TTS) models and conduct simple benchmarks.**\n", + "\n", + " \n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "32b09c71", + "metadata": { + "id": "FiCO4ikcDL2U", + "papermill": { + "duration": 0.046646, + "end_time": "2024-01-22T19:03:31.380063", + "exception": false, + "start_time": "2024-01-22T19:03:31.333417", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "# Models\n", + "\n", + "I will compare three model, two of these are open source and one is pay.\n", + "\n", + "OPEN SOURCE:\n", + "* [Bark](https://huggingface.co/docs/transformers/v4.36.1/en/model_doc/bark#optimizing-bark): Bark is a transformer-based text-to-speech model proposed by Suno AI in suno-ai/bark.\n", + "\n", + "* [Massively Multilingual Speech (MMS)](https://huggingface.co/docs/transformers/main/en/model_doc/mms): Is Multilingual and multitasking single model, it cans does:\n", + " * Automatic Speech Recognition (ASR) 1,107 languages\n", + " * **Speech Synthesis (TTS) 1,100 languages**\n", + " * Language Identification (LID) 4,017 languages\n", + "\n", + "\n", + "PAY API:\n", + "* [OPEN AI - TTS](https://platform.openai.com/docs/guides/text-to-speech)" + ] + }, + { + "cell_type": "markdown", + "id": "6631f2fe", + "metadata": { + "id": "UFvcD8NZ_9Sv", + "papermill": { + "duration": 0.095304, + "end_time": "2024-01-22T19:03:31.522145", + "exception": false, + "start_time": "2024-01-22T19:03:31.426841", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "# References and Resourcers\n", + "\n", + "* MMS\n", + "\n", + " * [MMS models](https://huggingface.co/models?sort=trending&search=facebook%2Fmms-tts) - MMS for diferent languages from Hugging Face Hub\n", + "\n", + " * [Complete Guide MMS](https://huggingface.co/docs/transformers/model_doc/mms)\n", + "\n", + "* Bark 🐶\n", + "\n", + " * [TTS: Text-To-Speech with Bark 🐶](https://www.kaggle.com/code/hinepo/tts-text-to-speech-with-bark) - from [@HinePo](https://www.kaggle.com/hinepo/code)\n", + "\n", + " * [Bark repository](https://github.com/suno-ai/bark) - git of bark model.\n", + "\n", + " * [Optimizing a Text-To-Speech model using 🤗 Transformers](https://huggingface.co/blog/optimizing-bark)\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "82f1b411", + "metadata": { + "id": "mNdyQX7zG2vC", + "papermill": { + "duration": 0.046424, + "end_time": "2024-01-22T19:03:31.617509", + "exception": false, + "start_time": "2024-01-22T19:03:31.571085", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "# LIBS" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "b44765f7", + "metadata": { + "execution": { + "iopub.execute_input": "2024-01-22T19:03:31.713504Z", + "iopub.status.busy": "2024-01-22T19:03:31.713124Z", + "iopub.status.idle": "2024-01-22T19:04:23.233895Z", + "shell.execute_reply": "2024-01-22T19:04:23.232824Z" + }, + "id": "7mJ1X5M-x_ms", + "outputId": "baf6260c-6f20-4310-8e8b-1b149b78d587", + "papermill": { + "duration": 51.571464, + "end_time": "2024-01-22T19:04:23.236373", + "exception": false, + "start_time": "2024-01-22T19:03:31.664909", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\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.\r\n", + "cudf 23.8.0 requires cupy-cuda11x>=12.0.0, which is not installed.\r\n", + "cuml 23.8.0 requires cupy-cuda11x>=12.0.0, which is not installed.\r\n", + "dask-cudf 23.8.0 requires cupy-cuda11x>=12.0.0, which is not installed.\r\n", + "apache-beam 2.46.0 requires dill<0.3.2,>=0.3.1.1, but you have dill 0.3.7 which is incompatible.\r\n", + "apache-beam 2.46.0 requires pyarrow<10.0.0,>=3.0.0, but you have pyarrow 11.0.0 which is incompatible.\r\n", + "cudf 23.8.0 requires pandas<1.6.0dev0,>=1.3, but you have pandas 2.0.3 which is incompatible.\r\n", + "cudf 23.8.0 requires protobuf<5,>=4.21, but you have protobuf 3.20.3 which is incompatible.\r\n", + "cuml 23.8.0 requires dask==2023.7.1, but you have dask 2023.12.1 which is incompatible.\r\n", + "cuml 23.8.0 requires distributed==2023.7.1, but you have distributed 2023.12.1 which is incompatible.\r\n", + "dask-cudf 23.8.0 requires dask==2023.7.1, but you have dask 2023.12.1 which is incompatible.\r\n", + "dask-cudf 23.8.0 requires distributed==2023.7.1, but you have distributed 2023.12.1 which is incompatible.\r\n", + "dask-cudf 23.8.0 requires pandas<1.6.0dev0,>=1.3, but you have pandas 2.0.3 which is incompatible.\r\n", + "jupyterlab 4.0.10 requires jupyter-lsp>=2.0.0, but you have jupyter-lsp 1.5.1 which is incompatible.\r\n", + "jupyterlab-lsp 5.0.1 requires jupyter-lsp>=2.0.0, but you have jupyter-lsp 1.5.1 which is incompatible.\r\n", + "pymc3 3.11.5 requires numpy<1.22.2,>=1.15.0, but you have numpy 1.24.3 which is incompatible.\r\n", + "pymc3 3.11.5 requires scipy<1.8.0,>=1.7.3, but you have scipy 1.11.4 which is incompatible.\r\n", + "tensorflow 2.13.0 requires typing-extensions<4.6.0,>=3.6.6, but you have typing-extensions 4.9.0 which is incompatible.\r\n", + "tensorflow-probability 0.21.0 requires typing-extensions<4.6.0, but you have typing-extensions 4.9.0 which is incompatible.\r\n", + "tensorflowjs 4.15.0 requires packaging~=23.1, but you have packaging 21.3 which is incompatible.\r\n", + "ydata-profiling 4.5.1 requires numpy<1.24,>=1.16.0, but you have numpy 1.24.3 which is incompatible.\r\n", + "ydata-profiling 4.5.1 requires pydantic<2,>=1.8.1, but you have pydantic 2.5.3 which is incompatible.\u001b[0m\u001b[31m\r\n", + "\u001b[0m" + ] + } + ], + "source": [ + "!pip install -U accelerate optimum transformers -qq\n", + "! pip install -U openai pydantic -qq" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "03a2c632", + "metadata": { + "execution": { + "iopub.execute_input": "2024-01-22T19:04:23.333683Z", + "iopub.status.busy": "2024-01-22T19:04:23.332723Z", + "iopub.status.idle": "2024-01-22T19:04:23.337524Z", + "shell.execute_reply": "2024-01-22T19:04:23.336569Z" + }, + "id": "PpFweX2i6fso", + "outputId": "694be355-d022-47c7-fc35-cc0bc963b6fc", + "papermill": { + "duration": 0.056467, + "end_time": "2024-01-22T19:04:23.339697", + "exception": false, + "start_time": "2024-01-22T19:04:23.283230", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "# from IPython.display import display_html\n", + "# display_html(\"\", raw=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "a6eb84a1", + "metadata": { + "execution": { + "iopub.execute_input": "2024-01-22T19:04:23.433937Z", + "iopub.status.busy": "2024-01-22T19:04:23.433256Z", + "iopub.status.idle": "2024-01-22T19:04:43.916033Z", + "shell.execute_reply": "2024-01-22T19:04:43.914940Z" + }, + "id": "zBwMpSiFmbEc", + "outputId": "a34aab66-f042-48fe-ba1b-6ba5a4b0e040", + "papermill": { + "duration": 20.531433, + "end_time": "2024-01-22T19:04:43.918191", + "exception": false, + "start_time": "2024-01-22T19:04:23.386758", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/conda/lib/python3.10/site-packages/scipy/__init__.py:146: UserWarning: A NumPy version >=1.16.5 and <1.23.0 is required for this version of SciPy (detected version 1.24.3\n", + " warnings.warn(f\"A NumPy version >={np_minversion} and <{np_maxversion}\"\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[nltk_data] Downloading package punkt to /usr/share/nltk_data...\n", + "[nltk_data] Package punkt is already up-to-date!\n", + "[nltk_data] Downloading package stopwords to /usr/share/nltk_data...\n", + "[nltk_data] Package stopwords is already up-to-date!\n", + "[nltk_data] Downloading package averaged_perceptron_tagger to\n", + "[nltk_data] /usr/share/nltk_data...\n", + "[nltk_data] Package averaged_perceptron_tagger is already up-to-\n", + "[nltk_data] date!\n", + "[nltk_data] Downloading package wordnet to /usr/share/nltk_data...\n", + "[nltk_data] Package wordnet is already up-to-date!\n", + "transformers 4.37.0\n" + ] + } + ], + "source": [ + "#general utils\n", + "import pandas as pd\n", + "import numpy as np\n", + "import wave\n", + "import nltk\n", + "import time\n", + "import warnings\n", + "import os\n", + "from tqdm.notebook import tqdm\n", + "import gc\n", + "\n", + "### HF\n", + "from transformers import AutoProcessor, AutoModel #BarkModel\n", + "from transformers import VitsModel,VitsTokenizer, AutoTokenizer, set_seed\n", + "import transformers\n", + "\n", + "#OpenAi\n", + "from openai import OpenAI\n", + "\n", + "### torch\n", + "import torch\n", + "\n", + "### audio\n", + "from IPython.display import Audio\n", + "\n", + "\n", + "\n", + "#Audio\n", + "from scipy.io.wavfile import write as write_wav\n", + "import librosa\n", + "import librosa.display\n", + "from IPython.display import Audio\n", + "\n", + "\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "\n", + "\n", + "import nltk\n", + "from nltk.corpus import stopwords\n", + "\n", + "\n", + "nltk.download('punkt')\n", + "nltk.download('stopwords')\n", + "nltk.download('averaged_perceptron_tagger')\n", + "nltk.download('wordnet')\n", + "\n", + "print('transformers',transformers.__version__)\n", + "warnings.filterwarnings('ignore')\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "38691c4f", + "metadata": { + "id": "gAImxxM5IRAR", + "papermill": { + "duration": 0.044262, + "end_time": "2024-01-22T19:04:44.015480", + "exception": false, + "start_time": "2024-01-22T19:04:43.971218", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "# Config" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "788bd7c5", + "metadata": { + "execution": { + "iopub.execute_input": "2024-01-22T19:04:44.108774Z", + "iopub.status.busy": "2024-01-22T19:04:44.107740Z", + "iopub.status.idle": "2024-01-22T19:04:44.205808Z", + "shell.execute_reply": "2024-01-22T19:04:44.204817Z" + }, + "id": "pAQJHni7ISoM", + "papermill": { + "duration": 0.147422, + "end_time": "2024-01-22T19:04:44.207946", + "exception": false, + "start_time": "2024-01-22T19:04:44.060524", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "class CFG:\n", + " DEBUG = False\n", + "\n", + " DEVICE = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n", + "\n", + " SEED= 42\n", + "\n", + "\n", + "\n", + " # Textos do build data - TROCAR PARA LANDING\n", + " INPUT_DATA = '/dbfs/mnt/landing-zone/TTS/conteudos_somenete_paragrafos.parquet'\n", + " AUDIO_OUT_PATH = '/dbfs/mnt/landing-zone/TTS/audios/mms/'\n", + "\n", + " # Output RAW - TROCAR PARA LANDING\n", + " MODEL_TYPE = 'mms-tts-por'\n", + "\n", + " # --------------------------------------\n", + " # Bark - if MODEL_TYPE == bark_batch\n", + " BARK_SPEAKER = \"v2/en_speaker_0\"\n", + " GEN_TEMP = 0.6\n", + " MIN_EOS_P = 0.01 # 0.01\n", + "\n", + "\n", + " #split Sentences\n", + " char_limit = 200 #if 0 use tokens_limit\n", + " tokens_limit = 0 #if 0 use char_limit\n", + "\n", + " ### post-processing: to visualize and remove Noise\n", + " AMPLITUDE_THRESHOLD = 0.05\n", + " TIME_THRESHOLD = int(24_000 * 0.5) # sample_rate * n_seconds\n", + " IGNORE_INITIAL_STEPS = int(24_000 * 0.5) # sample_rate * n_seconds\n", + "\n", + "\n", + " #OpenAI\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "0d0cd65c", + "metadata": { + "id": "7kvXZwWBHVjN", + "papermill": { + "duration": 0.046966, + "end_time": "2024-01-22T19:04:44.302661", + "exception": false, + "start_time": "2024-01-22T19:04:44.255695", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "# Text\n", + "\n", + "\n", + "In Greek mythology, there are multiple stories associated with the constellation Cancer, but one prominent tale involves the second labor of Heracles (Hercules in Roman mythology). Hera, the wife of Zeus and the goddess of marriage, held a grudge against Heracles because he was the illegitimate son of Zeus and another woman. To harm Heracles, Hera sent a giant crab named Karkinos to distract him during his battle with the Hydra, a serpent with multiple heads.\n", + "\n", + "As Heracles was fighting the Hydra, Karkinos latched onto his foot with its strong pincers. However, Heracles quickly crushed the crab with his foot, killing it. In recognition of Karkinos' loyalty and sacrifice, Hera placed the crab in the night sky as the constellation Cancer. This was her way of honoring the creature that tried to thwart Heracles in his quest. The Cancer constellation is often depicted as a crab in various interpretations of this myth.\n", + "\n", + "SOURCE: [TTS: Text-To-Speech with Bark 🐶](https://www.kaggle.com/code/hinepo/tts-text-to-speech-with-bark) - from [@HinePo](https://www.kaggle.com/hinepo/code)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "dff6770f", + "metadata": { + "execution": { + "iopub.execute_input": "2024-01-22T19:04:44.401109Z", + "iopub.status.busy": "2024-01-22T19:04:44.400547Z", + "iopub.status.idle": "2024-01-22T19:04:44.406476Z", + "shell.execute_reply": "2024-01-22T19:04:44.405509Z" + }, + "id": "67SA5LTP-HXo", + "papermill": { + "duration": 0.057314, + "end_time": "2024-01-22T19:04:44.408830", + "exception": false, + "start_time": "2024-01-22T19:04:44.351516", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "### choose text to make inference on\n", + "text_to_infer = '''\n", + "In Greek mythology, there are multiple stories associated with the constellation Cancer, but one prominent tale involves the second labor of Heracles (Hercules in Roman mythology). Hera, the wife of Zeus and the goddess of marriage, held a grudge against Heracles because he was the illegitimate son of Zeus and another woman. To harm Heracles, Hera sent a giant crab named Karkinos to distract him during his battle with the Hydra, a serpent with multiple heads.\n", + "\n", + "As Heracles was fighting the Hydra, Karkinos latched onto his foot with its strong pincers. However, Heracles quickly crushed the crab with his foot, killing it. In recognition of Karkinos' loyalty and sacrifice, Hera placed the crab in the night sky as the constellation Cancer. This was her way of honoring the creature that tried to thwart Heracles in his quest. The Cancer constellation is often depicted as a crab in various interpretations of this myth.\n", + "\n", + "'''" + ] + }, + { + "cell_type": "markdown", + "id": "b52129ed", + "metadata": { + "id": "iN1nnoozcLDR", + "papermill": { + "duration": 0.046612, + "end_time": "2024-01-22T19:04:44.504788", + "exception": false, + "start_time": "2024-01-22T19:04:44.458176", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "# Utils" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "16829424", + "metadata": { + "execution": { + "iopub.execute_input": "2024-01-22T19:04:44.598439Z", + "iopub.status.busy": "2024-01-22T19:04:44.598109Z", + "iopub.status.idle": "2024-01-22T19:04:44.612728Z", + "shell.execute_reply": "2024-01-22T19:04:44.611898Z" + }, + "id": "cqiIuSplcNEI", + "papermill": { + "duration": 0.063819, + "end_time": "2024-01-22T19:04:44.614616", + "exception": false, + "start_time": "2024-01-22T19:04:44.550797", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "def text_report(text, processor):\n", + "\n", + " n_tokens = len(processor.tokenizer.encode(text)) if MODEL_NAME == 'suno/bark' else len(processor.encode(text))\n", + " print('\\nText Report')\n", + " print('Nº of char: ', len(text))\n", + " print('Nº of words: ', len(nltk.word_tokenize(text)))\n", + " print('Nº of Sentences: ', len(nltk.sent_tokenize(text)))\n", + " print('Nº of Tokens: ', n_tokens)\n", + " print('\\n')\n", + "\n", + "def save_audio(path, audio, sample_rate):\n", + " scaled_audio_data = np.int16(np.array(audio) * 32767)\n", + " with wave.open(path, 'w') as wave_file:\n", + " wave_file.setnchannels(1) # mono\n", + " wave_file.setsampwidth(2) # 16-bit\n", + " wave_file.setframerate(sample_rate)\n", + " wave_file.writeframes(scaled_audio_data.tobytes())\n", + "\n", + "def measure_latency_and_memory_use(model, inputs, nb_loops = 5):\n", + "\n", + " # define Events that measure start and end of the generate pass\n", + " start_event = torch.cuda.Event(enable_timing=True)\n", + " end_event = torch.cuda.Event(enable_timing=True)\n", + "\n", + " # reset cuda memory stats and empty cache\n", + " torch.cuda.reset_peak_memory_stats(device)\n", + " torch.cuda.empty_cache()\n", + " torch.cuda.synchronize()\n", + "\n", + " # get the start time\n", + " start_event.record()\n", + "\n", + " # actually generate\n", + " for _ in range(nb_loops):\n", + " # set seed for reproducibility\n", + " set_seed(0)\n", + " output = model.generate(**inputs, do_sample = True, fine_temperature = 0.4, coarse_temperature = 0.8)\n", + "\n", + " # get the end time\n", + " end_event.record()\n", + " torch.cuda.synchronize()\n", + "\n", + " # measure memory footprint and elapsed time\n", + " max_memory = torch.cuda.max_memory_allocated(device)\n", + " elapsed_time = start_event.elapsed_time(end_event) * 1.0e-3\n", + "\n", + " print('Execution time:', elapsed_time/nb_loops, 'seconds')\n", + " print('Max memory footprint', max_memory*1e-9, ' GB')\n", + "\n", + " return output\n", + "\n", + "#from https://www.kaggle.com/code/hinepo/tts-text-to-speech-with-bark/notebook#%E2%9C%82%EF%B8%8F-Slice-array-function\n", + "def slice_array_wave(input_array, amplitude_threshold, time_threshold, ignore_initial_steps=0):\n", + " \"\"\"\n", + " Slice an input array based on consecutive low amplitude values.\n", + "\n", + " Parameters:\n", + " - input_array (numpy.ndarray): Input array containing amplitude values.\n", + " - amplitude_threshold (float): Threshold below which amplitudes are considered low.\n", + " - time_threshold (int): Number of consecutive low amplitude values needed to trigger slicing.\n", + " - ignore_initial_steps (int, optional): Number of initial steps to ignore before checking for consecutive low amplitudes.\n", + "\n", + " Returns:\n", + " numpy.ndarray: Sliced array up to the point where consecutive low amplitudes reach the specified time_threshold.\n", + " \"\"\"\n", + "\n", + " low_amplitude_indices = np.abs(input_array) < amplitude_threshold\n", + "\n", + " consecutive_count = 0\n", + " for i, is_low_amplitude in enumerate(low_amplitude_indices[ignore_initial_steps:]):\n", + " if is_low_amplitude:\n", + " consecutive_count += 1\n", + " else:\n", + " consecutive_count = 0\n", + "\n", + " if consecutive_count >= time_threshold:\n", + "# return input_array[:i - time_threshold]\n", + "# return input_array[:i - int(time_threshold/2) + int(time_threshold/4)]\n", + " return input_array[:i + int(time_threshold/4)]\n", + "\n", + " return input_array\n" + ] + }, + { + "cell_type": "markdown", + "id": "2bbb0e8e", + "metadata": { + "id": "dKKmiAUMH0cM", + "papermill": { + "duration": 0.045795, + "end_time": "2024-01-22T19:04:44.705450", + "exception": false, + "start_time": "2024-01-22T19:04:44.659655", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "# Get Model\n", + "\n", + "Bark uses a processor to prepare text to the model, and it has tokenizer was a method of processing. MMS just uses the tokenizer.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "c88f3587", + "metadata": { + "execution": { + "iopub.execute_input": "2024-01-22T19:04:44.802602Z", + "iopub.status.busy": "2024-01-22T19:04:44.801916Z", + "iopub.status.idle": "2024-01-22T19:04:44.812373Z", + "shell.execute_reply": "2024-01-22T19:04:44.811485Z" + }, + "id": "txHNksUIH4dE", + "papermill": { + "duration": 0.061421, + "end_time": "2024-01-22T19:04:44.814434", + "exception": false, + "start_time": "2024-01-22T19:04:44.753013", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "def get_model(model_name):\n", + "\n", + "\n", + " if model_name == 'facebook/mms-tts-eng':\n", + "\n", + " processor = VitsTokenizer.from_pretrained(model_name,\n", + " #noramlize=False\n", + " )\n", + "\n", + " model = VitsModel.from_pretrained(model_name).to(CFG.DEVICE)\n", + "\n", + "\n", + " #model.resize_token_embeddings(len(processor))\n", + "\n", + "\n", + " print('\\nModel Report\\n')\n", + " print('Name = ',model_name)\n", + " print('Sample Rate = ',model.config.sampling_rate)\n", + " print('Noise Sacle = ',model.noise_scale)\n", + " print('Sepaking Rate = ', model.speaking_rate)\n", + " print('Number os Speakers = ', model.config.speaker_embedding_size + 1)\n", + " print('Context Lenght = ', processor.model_max_length)\n", + "\n", + "\n", + " sample_rate = model.config.sampling_rate\n", + "\n", + " elif model_name == 'suno/bark':\n", + "\n", + " model = AutoModel.from_pretrained(model_name,\n", + " torch_dtype = torch.float16, # half-precision\n", + " ).to(CFG.DEVICE)\n", + "\n", + "\n", + "\n", + " processor = AutoProcessor.from_pretrained(model_name,\n", + " voice_preset=CFG.BARK_SPEAKER,\n", + " temp=CFG.GEN_TEMP,\n", + " min_eos_p=CFG.MIN_EOS_P)\n", + "\n", + "\n", + " print(f'{model_name} Report\\n')\n", + " print('Sample Rate = ', model.generation_config.sample_rate)\n", + " print('Sepaker = ', CFG.BARK_SPEAKER)\n", + " print('Context Lenght = ', processor.tokenizer.model_max_length)\n", + "\n", + " sample_rate = model.generation_config.sample_rate\n", + "\n", + "\n", + " #load not use models on cpu\n", + " model.enable_cpu_offload()\n", + "\n", + " # model = model.to_bettertransformer()\n", + "\n", + " else:\n", + " print('Invalid model name')\n", + "\n", + "\n", + " return model, processor, sample_rate\n" + ] + }, + { + "cell_type": "markdown", + "id": "ace1e938", + "metadata": { + "id": "fzL2RmSXy8gb", + "papermill": { + "duration": 0.044255, + "end_time": "2024-01-22T19:04:44.904871", + "exception": false, + "start_time": "2024-01-22T19:04:44.860616", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "# Inferences" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "1bdc20d5", + "metadata": { + "execution": { + "iopub.execute_input": "2024-01-22T19:04:44.996410Z", + "iopub.status.busy": "2024-01-22T19:04:44.996090Z", + "iopub.status.idle": "2024-01-22T19:04:45.008785Z", + "shell.execute_reply": "2024-01-22T19:04:45.007746Z" + }, + "id": "sMipQshWy9wq", + "papermill": { + "duration": 0.060606, + "end_time": "2024-01-22T19:04:45.010828", + "exception": false, + "start_time": "2024-01-22T19:04:44.950222", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "def inference(text, processor = None, model=None):\n", + " #reset cuda memory stats and empty cache\n", + " # torch.cuda.reset_peak_memory_stats(CFG.DEVICE)\n", + " # torch.cuda.empty_cache()\n", + "\n", + " if MODEL_NAME == 'facebook/mms-tts-eng':\n", + "\n", + "\n", + "\n", + "\n", + " set_seed(CFG.SEED)\n", + "\n", + "\n", + "\n", + " text_report(text, processor)\n", + "\n", + " start_time = time.time()\n", + "\n", + " inputs = processor(text, return_tensors=\"pt\").to(CFG.DEVICE)\n", + " with torch.no_grad():\n", + " output = model(**inputs)\n", + "\n", + "\n", + "\n", + " max_memory = torch.cuda.max_memory_allocated(CFG.DEVICE)\n", + "\n", + " end_time = time.time()\n", + " run_time = end_time - start_time\n", + " run_time = round(end_time - start_time,2)\n", + "\n", + " #audio = output['waveform'].cpu().numpy().squeeze()\n", + " print('inference Time = ', run_time,'seconds')\n", + " print('Max memory footprint', max_memory*1e-9, ' GB')\n", + "\n", + " return output, run_time\n", + "\n", + " elif MODEL_NAME == 'suno/bark':\n", + "\n", + "\n", + " text_report(text, processor)\n", + "\n", + " start_time = time.time()\n", + "\n", + " sentences = nltk.sent_tokenize(text)\n", + "\n", + "\n", + " inputs = processor(\n", + " text = sentences,\n", + " return_tensors = \"pt\",\n", + " return_attention_mask = True,\n", + " max_length = 1024,\n", + " voice_preset = CFG.BARK_SPEAKER,\n", + " add_special_tokens = False,\n", + " ).to(CFG.DEVICE)\n", + "\n", + " with torch.inference_mode():\n", + " output = model.generate(\n", + " **inputs,\n", + " do_sample = True,\n", + " semantic_max_new_tokens = 1024,\n", + " pad_token_id = processor.tokenizer.pad_token_id,\n", + " )\n", + "\n", + " torch.cuda.synchronize()\n", + "\n", + " max_memory = torch.cuda.max_memory_allocated(CFG.DEVICE)\n", + "\n", + " end_time = time.time()\n", + " run_time = round(end_time - start_time,2)\n", + "\n", + " print('inference Time = ', run_time,'seconds')\n", + " print('Max memory footprint', max_memory*1e-9, ' GB')\n", + "\n", + " return output.cpu(),run_time\n", + "\n", + " elif MODEL_NAME == 'tts-1':\n", + "\n", + " client = OpenAI(api_key = OPENAI_API_KEY)\n", + "\n", + " start_time = time.time()\n", + " response = client.audio.speech.create(\n", + " model = MODEL_NAME,\n", + " voice = 'onyx',\n", + " input = text,\n", + " )\n", + "\n", + " end_time = time.time()\n", + " run_time = round(end_time - start_time,2)\n", + "\n", + " response.stream_to_file(\"./openai_audio.mp3\")\n", + "\n", + " print('inference Time = ', run_time,'seconds')\n", + "\n", + " return response,run_time" + ] + }, + { + "cell_type": "markdown", + "id": "f5e4ca58", + "metadata": { + "id": "wL6MWshjuwXJ", + "papermill": { + "duration": 0.045456, + "end_time": "2024-01-22T19:04:45.101731", + "exception": false, + "start_time": "2024-01-22T19:04:45.056275", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "# Bark - Infe\n", + "\n", + "Bark has its output limited:\n", + "\n", + " \n", + "\n", + "Because that, it's necessary split the text in chunk of sentences and after that concatenate and apply a post processing the output audio of each sentence.\n", + "\n", + "I used the post processing and audio concatenation functions from [TTS: Text-To-Speech with Bark 🐶](https://www.kaggle.com/code/hinepo/tts-text-to-speech-with-bark) - thanks to [@HinePo](https://www.kaggle.com/hinepo/code) for you helpful notebook.\n", + "\n", + "I also used optimization techniques like: Better Transform, Half-precision and CPU offload, Check the code in `get_model` and `inference` functions. Check [Optimizing a Text-To-Speech model using 🤗 Transformers](https://huggingface.co/blog/optimizing-bark) for more information\n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "54cb1f22", + "metadata": { + "execution": { + "iopub.execute_input": "2024-01-22T19:04:45.194032Z", + "iopub.status.busy": "2024-01-22T19:04:45.193692Z", + "iopub.status.idle": "2024-01-22T19:05:10.470426Z", + "shell.execute_reply": "2024-01-22T19:05:10.469604Z" + }, + "id": "raYYNpxvtVLy", + "outputId": "de655315-03a4-4aa2-b66f-6bb008419a9f", + "papermill": { + "duration": 25.325145, + "end_time": "2024-01-22T19:05:10.472850", + "exception": false, + "start_time": "2024-01-22T19:04:45.147705", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "e0b62456f7454b24bc168f0fa266a06a", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "config.json: 0%| | 0.00/8.81k [00:00\n", + " \n", + " 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"final_audio_list = []\n", + "for i in range(output.shape[0]):\n", + "\n", + " raw_audio = output[i]\n", + " processing_audio = slice_array_wave(\n", + " input_array = raw_audio,\n", + " amplitude_threshold = CFG.AMPLITUDE_THRESHOLD,\n", + " time_threshold = CFG.TIME_THRESHOLD,\n", + " ignore_initial_steps = CFG.IGNORE_INITIAL_STEPS\n", + " )\n", + "\n", + " final_audio_list.append(processing_audio.cpu().numpy())\n", + "\n", + "\n", + "\n", + " print(f'\\nSentence : {i+1}')\n", + " print(' Raw Audio')\n", + " display(Audio(raw_audio, rate=sample_rate))\n", + " print(' Processing Audio')\n", + " display(Audio(processing_audio, rate=sample_rate))\n", + "\n", + "final_bark_audio = np.concatenate(final_audio_list)\n", + "save_audio('./bark_audio.wav', final_bark_audio, sample_rate)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "c8f6edd7", + "metadata": { + "execution": { + "iopub.execute_input": "2024-01-22T19:06:46.247173Z", + "iopub.status.busy": "2024-01-22T19:06:46.246440Z", + "iopub.status.idle": "2024-01-22T19:06:46.304240Z", + "shell.execute_reply": "2024-01-22T19:06:46.302976Z" + }, + "id": "bwvIQxmWngTO", + "outputId": "70e9d269-fce6-43ed-e070-648b8b64b68a", + "papermill": { + "duration": 0.31345, + "end_time": "2024-01-22T19:06:46.348943", + "exception": false, + "start_time": "2024-01-22T19:06:46.035493", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "display(Audio(filename = './bark_audio.wav', rate=sample_rate))" + ] + }, + { + "cell_type": "markdown", + "id": "41b2b79c", + "metadata": { + "id": "EHWNw-hafsjx", + "papermill": { + "duration": 0.273867, + "end_time": "2024-01-22T19:06:46.897319", + "exception": false, + "start_time": "2024-01-22T19:06:46.623452", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "There are some noises in initial of sentences." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "c995c4c0", + "metadata": { + "execution": { + "iopub.execute_input": "2024-01-22T19:06:47.428382Z", + "iopub.status.busy": "2024-01-22T19:06:47.427981Z", + "iopub.status.idle": "2024-01-22T19:06:47.463369Z", + "shell.execute_reply": "2024-01-22T19:06:47.462421Z" + }, + "id": "kLwo6orjuBHy", + "papermill": { + "duration": 0.303493, + "end_time": "2024-01-22T19:06:47.465437", + "exception": false, + "start_time": "2024-01-22T19:06:47.161944", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "del bark_model, processor, sample_rate" + ] + }, + { + "cell_type": "markdown", + "id": "660eb25c", + "metadata": { + "id": "7pLIjsMjuMAH", + "papermill": { + "duration": 0.254711, + "end_time": "2024-01-22T19:06:47.975398", + "exception": false, + "start_time": "2024-01-22T19:06:47.720687", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "# MMS" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "33e31b99", + "metadata": { + "execution": { + "iopub.execute_input": "2024-01-22T19:06:48.504628Z", + "iopub.status.busy": "2024-01-22T19:06:48.504053Z", + "iopub.status.idle": "2024-01-22T19:06:50.476406Z", + "shell.execute_reply": "2024-01-22T19:06:50.475368Z" + }, + "id": "dW9tctLZuUQo", + "outputId": "51af64b6-a6a4-4610-d4f3-32c0a4c99115", + "papermill": { + "duration": 2.236297, + "end_time": "2024-01-22T19:06:50.478518", + "exception": false, + "start_time": "2024-01-22T19:06:48.242221", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "9a1db94467e44ecf8462de701ed44dd6", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "tokenizer_config.json: 0%| | 0.00/287 [00:00\n", + " \n", + " Your browser does not support the audio element.\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "print('MMS output: ', output.keys(), '\\n')\n", + "mms_audio = output['waveform'].cpu().numpy()\n", + "save_audio('./mms_audio.wav', mms_audio, sample_rate)\n", + "display(Audio(filename = './mms_audio.wav', rate=sample_rate))" + ] + }, + { + "cell_type": "markdown", + "id": "ef2febd7", + "metadata": { + "id": "mYeD1PQFgg1q", + "papermill": { + "duration": 0.28771, + "end_time": "2024-01-22T19:06:55.017628", + "exception": false, + "start_time": "2024-01-22T19:06:54.729918", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "The model ignores punctuations.\n", + "\n", + "\n", + "\n", + "For more details about Pre-processing check the [Paper](https://arxiv.org/abs/2305.13516)" + ] + }, + { + "cell_type": "markdown", + "id": "ae1fabd4", + "metadata": { + "id": "UIxZzjOilmQ-", + "papermill": { + "duration": 0.292571, + "end_time": "2024-01-22T19:06:55.597935", + "exception": false, + "start_time": "2024-01-22T19:06:55.305364", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "# OpenAI - TTS\n", + "\n", + "There are two model variate in OpenAI TTS API:\n", + "\n", + "\n", + "\n", + "\n", + "There are some types of voices, `alloy, echo, fable, onyx, nova, and shimmer`, check samples of these voices in english [here](https://platform.openai.com/docs/guides/text-to-speech)\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "9ce34a95", + "metadata": { + "papermill": { + "duration": 0.296206, + "end_time": "2024-01-22T19:06:56.190108", + "exception": false, + "start_time": "2024-01-22T19:06:55.893902", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "## Key" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "bc6be3f2", + "metadata": { + "execution": { + "iopub.execute_input": "2024-01-22T19:06:56.796684Z", + "iopub.status.busy": "2024-01-22T19:06:56.795862Z", + "iopub.status.idle": "2024-01-22T19:06:56.974886Z", + "shell.execute_reply": "2024-01-22T19:06:56.973884Z" + }, + "id": "E3PK_krzlok_", + "papermill": { + "duration": 0.487886, + "end_time": "2024-01-22T19:06:56.977325", + "exception": false, + "start_time": "2024-01-22T19:06:56.489439", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "from kaggle_secrets import UserSecretsClient\n", + "user_secrets = UserSecretsClient()\n", + "OPENAI_API_KEY = user_secrets.get_secret(\"openai\")" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "f56e7581", + "metadata": { + "execution": { + "iopub.execute_input": "2024-01-22T19:06:57.583160Z", + "iopub.status.busy": "2024-01-22T19:06:57.582368Z", + "iopub.status.idle": "2024-01-22T19:07:16.527997Z", + "shell.execute_reply": "2024-01-22T19:07:16.526900Z" + }, + "id": "L8r4r6EI9FRH", + "outputId": "b608e2f1-f0c1-47c8-dfab-61a4261703ad", + "papermill": { + "duration": 19.249407, + "end_time": "2024-01-22T19:07:16.530044", + "exception": false, + "start_time": "2024-01-22T19:06:57.280637", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "inference Time = 18.93 seconds\n" + ] + } + ], + "source": [ + "MODEL_NAME = \"tts-1\"\n", + "output, run_time = inference(text_to_infer)\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "c8be5ddb", + "metadata": { + "id": "U2Ak6U2dhvRo", + "papermill": { + "duration": 0.295721, + "end_time": "2024-01-22T19:07:17.132051", + "exception": false, + "start_time": "2024-01-22T19:07:16.836330", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "The inference time of OpenAI API is less than bark but, is more bigger than MMS. The OpenAI TTS models are optimized to:\n", + "\n", + "* Narrate a written blog post\n", + "* Produce spoken audio in multiple languages\n", + "* Give real time audio output using streaming\n" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "90b7bbb4", + "metadata": { + "execution": { + "iopub.execute_input": "2024-01-22T19:07:17.734006Z", + "iopub.status.busy": "2024-01-22T19:07:17.733142Z", + "iopub.status.idle": "2024-01-22T19:07:17.739100Z", + "shell.execute_reply": "2024-01-22T19:07:17.738355Z" + }, + "id": "uElGkZF9g8R6", + "papermill": { + "duration": 0.306413, + "end_time": "2024-01-22T19:07:17.741231", + "exception": false, + "start_time": "2024-01-22T19:07:17.434818", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "output.stream_to_file(\"./openai_audio.mp3\")" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "77f30808", + "metadata": { + "execution": { + "iopub.execute_input": "2024-01-22T19:07:18.340720Z", + "iopub.status.busy": "2024-01-22T19:07:18.340291Z", + "iopub.status.idle": "2024-01-22T19:07:18.366846Z", + "shell.execute_reply": "2024-01-22T19:07:18.365910Z" + }, + "id": "3nzywo8HhHqy", + "outputId": "a9dd2dca-2b73-4a12-921f-f7a456e3a261", + "papermill": { + "duration": 0.345937, + "end_time": "2024-01-22T19:07:18.387718", + "exception": false, + "start_time": "2024-01-22T19:07:18.041781", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "display(Audio(filename=\"./openai_audio.mp3\", rate=24000))" + ] + }, + { + "cell_type": "markdown", + "id": "51a6e87d", + "metadata": { + "id": "NnPXUnW_iURN", + "papermill": { + "duration": 0.328431, + "end_time": "2024-01-22T19:07:19.040434", + "exception": false, + "start_time": "2024-01-22T19:07:18.712003", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "The result has a studio quality 😮, that is impressive. In other tests, I was check that the context length of Open TTS model is 4096 characters." + ] + }, + { + "cell_type": "markdown", + "id": "0c2b0b5c", + "metadata": { + "execution": { + "iopub.execute_input": "2024-01-19T17:57:04.911784Z", + "iopub.status.busy": "2024-01-19T17:57:04.911169Z", + "iopub.status.idle": "2024-01-19T17:57:04.926389Z", + "shell.execute_reply": "2024-01-19T17:57:04.925078Z", + "shell.execute_reply.started": "2024-01-19T17:57:04.911752Z" + }, + "id": "f79c8Xtvhhca", + "papermill": { + "duration": 0.320702, + "end_time": "2024-01-22T19:07:19.681510", + "exception": false, + "start_time": "2024-01-22T19:07:19.360808", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "# Conclusion\n", + "\n", + "\n", + "| Model | Inference Time (s) | Audio Quality (s) | optimization task (s) | Deploy |\n", + "| ------------- | ------------- | ------------- | ------------- | ------------- |\n", + "| Bark | ~ 98 | Good | Customization, Resharch and Finetuning | Probably isn't scalable |\n", + "| MMS | ~ 1.5 | understandable | Real Time Language translation | GPU and \"complex\" infra |\n", + "| OpanAI | ~ 11 | Studio | Faster TTS for speech with studio quality | \"Simple deploy\" (just API)\n", + "\n", + "
\n", + "\n", + "The best Model depends of de aplication.\n", + "\n", + "* If it is a real time aplication, or extremaly quality isn't a requirement, go a head with MMS\n", + "* If the client needs that the speech is much understandable and had a studio quality, like audio books or audio news for example, the API of OpenAI can be a good choice.\n", + "* For resharch, train and finetuning. Bark can be a good choice." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "0ed2772e", + "metadata": { + "execution": { + "iopub.execute_input": "2024-01-22T19:07:20.328700Z", + "iopub.status.busy": "2024-01-22T19:07:20.327655Z", + "iopub.status.idle": "2024-01-22T19:07:20.438247Z", + "shell.execute_reply": "2024-01-22T19:07:20.437100Z" + }, + "id": "qjebTdcmR9Cl", + "outputId": "f1d58089-e4ad-4935-91e7-245fc5a90735", + "papermill": { + "duration": 0.476222, + "end_time": "2024-01-22T19:07:20.477820", + "exception": false, + "start_time": "2024-01-22T19:07:20.001598", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Bark Audio\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "MMS Audio\n" + ] + }, + { + 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