{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "id": "OiFxAw6MoHnp" }, "outputs": [], "source": [ "# Locally, we recommend using `uv` over `pip`\n", "!pip install pocket-tts" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "FfBHOrFcU0gI" }, "outputs": [], "source": [ "import scipy.io.wavfile\n", "from IPython.display import Audio\n", "\n", "from pocket_tts import TTSModel\n", "\n", "# The pre-defined voices are alba, marius, javert, jean, fantine, cosette, eponine, and azelma.\n", "# You can also clone any voice you'd like by passing a file path!\n", "# For voice cloning, please go to https://huggingface.co/kyutai/pocket-tts\n", "# and accept the terms, then make sure you're logged in locally with `uvx hf auth login`.\n", "voice = \"alba\"\n", "\n", "tts_model = TTSModel.load_model()\n", "voice_state = tts_model.get_state_for_audio_prompt(voice)\n", "\n", "# Audio is a 1D torch tensor containing PCM data\n", "audio = tts_model.generate_audio(voice_state, \"Hello world, this is a test.\")\n", "\n", "# You can also find the output in the files tab on the left\n", "scipy.io.wavfile.write(\"output.wav\", tts_model.sample_rate, audio.numpy())\n", "\n", "Audio(audio.numpy(), rate=tts_model.sample_rate)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "gbnHfLwbo5W8" }, "outputs": [], "source": [] } ], "metadata": { "colab": { "provenance": [] }, "kernelspec": { "display_name": "pocket-tts", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.16" } }, "nbformat": 4, "nbformat_minor": 0 }