Inference.ipynb

{
 "cells": [
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    {
     "name": "stderr",
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     "text": [
      "/home/chiayu/miniconda3/envs/flux/lib/python3.11/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n",
      "/home/chiayu/miniconda3/envs/flux/lib/python3.11/site-packages/transformers/utils/hub.py:127: FutureWarning: Using `PYTORCH_TRANSFORMERS_CACHE` is deprecated and will be removed in v5 of Transformers. Use `HF_HOME` instead.\n",
      "  warnings.warn(\n",
      "2024-12-31 11:31:40.419075: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:485] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
      "2024-12-31 11:31:40.430545: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:8454] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
      "2024-12-31 11:31:40.434019: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1452] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
      "2024-12-31 11:31:40.442720: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
      "To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
      "2024-12-31 11:31:41.173874: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n",
      "/home/chiayu/miniconda3/envs/flux/lib/python3.11/site-packages/torch/nn/utils/weight_norm.py:134: FutureWarning: `torch.nn.utils.weight_norm` is deprecated in favor of `torch.nn.utils.parametrizations.weight_norm`.\n",
      "  WeightNorm.apply(module, name, dim)\n",
      "Fetching 4 files:  25%|██▌       | 1/4 [00:00<00:01,  1.86it/s]"
     ]
    }
   ],
   "source": [
    "import torchaudio\n",
    "from tangoflux import TangoFluxInference\n",
    "from IPython.display import Audio\n",
    "model = TangoFluxInference(name='declare-lab/TangoFlux')\n",
    "\n",
    "\n",
    "audio = model.generate('Hammer slowly hitting the wooden table',steps=50,duration=10)\n",
    "\n",
    "Audio(data=audio,rate=44100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "torchaudio.save('temp.wav', audio, sample_rate=44100)"
   ]
  }
 ],
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