File size: 7,444 Bytes
8bdc19f |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 |
{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"id": "a84125d0-ae92-4829-976d-456491ac7c0a",
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"0\""
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "a3179777-b19d-46fb-8eb5-c5a4dd175f68",
"metadata": {},
"outputs": [],
"source": [
"from vinorm import TTSnorm\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "e9427640-6c1e-4337-9230-30838b508ea8",
"metadata": {},
"outputs": [],
"source": [
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "189956af-477b-4756-88e6-e3002a8ed039",
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"from f5tts_wrapper import F5TTSWrapper\n",
"\n",
"# Initialize the F5-TTS wrapper\n",
"tts = F5TTSWrapper(\n",
" vocoder_name=\"vocos\",\n",
" # point to EraX model checkpoint downloaded from HuggingFace\n",
" ckpt_path=\"path to model_last.pt\",\n",
" vocab_file=\"vocab.txt\",\n",
" use_ema=False,\n",
")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "1ccf4419-a9b7-4dcb-9ce0-5ab3b948de61",
"metadata": {},
"outputs": [],
"source": [
"ref_audio_path = \"update_213000_ref.wav\""
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "240415b2-5e32-4caa-b833-ceae50675ca3",
"metadata": {},
"outputs": [],
"source": [
"import IPython\n",
"IPython.display.Audio(ref_audio_path)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "6bde9c39-d8fa-454a-9fcc-418175d8a989",
"metadata": {},
"outputs": [],
"source": [
"ref_text = \"Thậm chí không ăn thì cũng có cảm giác rất là cứng bụng, chủ yếu là cái phần rốn...trở lên. Em có cảm giác khó thở, và ngủ cũng không ngon, thường bị ợ hơi rất là nhiều\"\n",
"ref_text = TTSnorm(ref_text)\n",
"ref_text"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "1587e309-d118-40f1-91ee-a949a28eb135",
"metadata": {},
"outputs": [],
"source": [
"text = \"Trong khi đó, tại một chung cư trên địa bàn P.Vĩnh Tuy (Q.Hoàng Mai), nhiều người sống trên tầng cao giật mình khi thấy rung lắc mạnh nên đã chạy xuống sảnh tầng 1. Cư dân tại đây cho biết, họ chưa bao giờ cảm thấy ảnh hưởng của động đất mạnh như hôm nay.\"\n",
"text_norm = TTSnorm(text)\n",
"text_norm"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "918bf198-1ec7-4236-87bd-3be6210ea011",
"metadata": {},
"outputs": [],
"source": [
"# Path to reference audio and output directory\n",
"output_dir = \"output\"\n",
"os.makedirs(output_dir, exist_ok=True)\n",
"\n",
"# Preprocess the reference audio just once\n",
"# You can provide a reference text, or leave it blank for auto-transcription\n",
"tts.preprocess_reference(\n",
" ref_audio_path=ref_audio_path,\n",
" ref_text=ref_text, # Leave empty for auto-transcription\n",
" clip_short=True # Clip long audio to ~12 seconds max\n",
")\n",
"\n",
"print(f\"Reference audio duration: {tts.get_current_audio_length():.2f} seconds\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "23aa55ad-612f-42f9-9742-ce0ce50a054a",
"metadata": {},
"outputs": [],
"source": [
"sentences = [\n",
" gen_text\n",
"]\n",
"for i, sentence in enumerate(sentences):\n",
" output_path = os.path.join(output_dir, f\"generated_non_ema_{i+1}.wav\")\n",
"\n",
" sentence_normed = TTSnorm(sentence)\n",
" \n",
" # Generate the audio\n",
" tts.generate(\n",
" text=sentence_normed,\n",
" output_path=output_path,\n",
" nfe_step=20, # Number of denoising steps (32)\n",
" cfg_strength=2.0, # Classifier-free guidance strength (2)\n",
" speed=1.0, # Speed of generated speech (1.0)\n",
" cross_fade_duration=0.15, # Cross-fade between chunks (0.15)\n",
" )\n",
" \n",
" print(f\"Generated: {output_path}\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "7a3cb66e-1343-43cd-bed7-84254a74262f",
"metadata": {},
"outputs": [],
"source": [
"import IPython\n",
"IPython.display.Audio(f\"{output_path}\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "832f3426-7a2a-4be9-aaaf-630866ecb31b",
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"# For applications needing the raw audio data rather than saved files:\n",
"audio_array, sample_rate = tts.generate(\n",
" text=gen_text,\n",
" return_numpy=True,\n",
" nfe_step=20, # Number of denoising steps (32)\n",
" cfg_strength=2.0, # Classifier-free guidance strength (2)\n",
" speed=1.0, # Speed of generated speech (1.0)\n",
" cross_fade_duration=0.15, # Cross-fade between chunks (0.15)\n",
")\n",
"print(f\"Generated audio array with shape {audio_array.shape} and sample rate {sample_rate}\")\n",
"\n",
"# Get audio and spectrogram (useful for visualization)\n",
"audio_array, sample_rate, spectrogram = tts.generate(\n",
" text=\"This returns both audio and its spectrogram.\",\n",
" return_numpy=True,\n",
" return_spectrogram=True,\n",
" nfe_step=20, # Number of denoising steps (32)\n",
" cfg_strength=2.0, # Classifier-free guidance strength (2)\n",
" speed=1.0, # Speed of generated speech (1.0)\n",
" cross_fade_duration=0.15, # Cross-fade between chunks (0.15)\n",
")\n",
"print(f\"Generated spectrogram with shape {spectrogram.shape}\")\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "862de193-96b3-4a37-9326-697838168437",
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"def save_spectrogram(spectrogram):\n",
" plt.figure(figsize=(12, 4))\n",
" plt.imshow(spectrogram, origin=\"lower\", aspect=\"auto\")\n",
" plt.colorbar()\n",
" plt.show()\n",
" plt.close()\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "fb1979e7-5645-4cb0-94d8-aacc59109b03",
"metadata": {},
"outputs": [],
"source": [
"save_spectrogram(spectrogram)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "87824cb4-7721-4d46-948a-a3457869b679",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"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": "3.10.15"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
|