{
"cells": [
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"data = [{\"captions\": \"Rhythmic wooden table tapping overlaid with steady water pouring sound\", \"location\": \"data/test.wav\", \"duration\": 10.0} for _ in range(10)]"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"from src.utils import read_wav_file\n",
"\n",
"wav = read_wav_file('data/test.wav',30)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"torch.Size([1, 1323000])\n"
]
}
],
"source": [
"print(wav.shape)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"import json\n",
"with open('data/val.json','w') as f:\n",
" json.dump(data,f)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import torchaudio\n",
"from TangoFlux import TangoFluxInference\n",
"from IPython.display import Audio\n",
"model = TangoFluxInference(name='declare-lab/TangoFlux')\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
" 0%| | 0/25 [00:00, ?it/s]"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
" 0%| | 0/25 [00:01, ?it/s]\n"
]
},
{
"data": {
"text/html": [
"\n",
" \n",
" "
],
"text/plain": [
""
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"audio = model.generate('Hammer slowly hitting the wooden table',steps=25,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)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "flux",
"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": "3.11.10"
}
},
"nbformat": 4,
"nbformat_minor": 2
}