Spaces:
Sleeping
Sleeping
File size: 12,652 Bytes
9e3182d 235e7c7 bebc496 9e3182d 68f40ec bebc496 b9d2659 235e7c7 c849c89 8fd86b6 f2fdc48 8fd86b6 9e3182d 6e7a5e3 9e3182d f2fdc48 8fd86b6 cb04900 9e3182d 8fd86b6 9e3182d f2fdc48 c68ab45 f2fdc48 8fd86b6 f2fdc48 235e7c7 f2fdc48 8fd86b6 96c6f9a f2fdc48 96c6f9a f2fdc48 96c6f9a 90a97f4 f2fdc48 90a97f4 f2fdc48 96c6f9a 90a97f4 f2fdc48 96c6f9a 8fd86b6 f2fdc48 8fd86b6 b9d2659 235e7c7 c68ab45 8fd86b6 b9d2659 8fd86b6 b9d2659 f2fdc48 8fd86b6 f2fdc48 b9bf9b2 9e3182d 8fd86b6 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 |
import os
os.environ["NUMBA_DISABLE_CACHE"] = "1"
import os
import gradio as gr
from docx import Document
from TTS.api import TTS
import tempfile
import zipfile
from io import BytesIO
import re
from pydub import AudioSegment
final_audio = AudioSegment.empty()
from pydub import AudioSegment
from bark import generate_audio # Importing Bark
# Voice model
VOICE_MODEL = "tts_models/en/vctk/vits"
# Embedded metadata (from your file)
SPEAKER_METADATA = {
300: { "age": 23, "gender": "F", "accent": "American"},
271: { "age": 19, "gender": "M", "accent": "Scottish"},
287: { "age": 23, "gender": "M", "accent": "English"},
262: { "age": 23, "gender": "F", "accent": "Scottish"},
284: { "age": 20, "gender": "M", "accent": "Scottish"},
297: { "age": 20, "gender": "F", "accent": "American"},
227: { "age": 38, "gender": "M", "accent": "English"},
246: { "age": 22, "gender": "M", "accent": "Scottish"},
225: { "age": 23, "gender": "F", "accent": "English"},
259: { "age": 23, "gender": "M", "accent": "English"},
252: { "age": 22, "gender": "M", "accent": "Scottish"},
231: { "age": 23, "gender": "F", "accent": "English"},
266: { "age": 22, "gender": "F", "accent": "Irish"},
241: { "age": 21, "gender": "M", "accent": "Scottish"},
312: { "age": 19, "gender": "F", "accent": "Canadian"},
329: { "age": 23, "gender": "F", "accent": "American"},
232: { "age": 23, "gender": "M", "accent": "English"},
305: { "age": 19, "gender": "F", "accent": "American"},
311: { "age": 21, "gender": "M", "accent": "American"},
301: { "age": 23, "gender": "F", "accent": "American"},
304: { "age": 22, "gender": "M", "accent": "NorthernIrish"},
310: { "age": 21, "gender": "F", "accent": "American"},
260: { "age": 21, "gender": "M", "accent": "Scottish"},
315: { "age": 18, "gender": "M", "accent": "American"},
374: { "age": 28, "gender": "M", "accent": "Australian"},
364: { "age": 23, "gender": "M", "accent": "Irish"},
269: { "age": 20, "gender": "F", "accent": "English"},
345: { "age": 22, "gender": "M", "accent": "American"},
326: { "age": 26, "gender": "M", "accent": "Australian"},
343: { "age": 27, "gender": "F", "accent": "Canadian"},
230: { "age": 22, "gender": "F", "accent": "English"},
376: { "age": 22, "gender": "M", "accent": "Indian"},
240: { "age": 21, "gender": "F", "accent": "English"},
298: { "age": 19, "gender": "M", "accent": "Irish"},
272: { "age": 23, "gender": "M", "accent": "Scottish"},
248: { "age": 23, "gender": "F", "accent": "Indian"},
264: { "age": 23, "gender": "F", "accent": "Scottish"},
250: { "age": 22, "gender": "F", "accent": "English"},
292: { "age": 23, "gender": "M", "accent": "NorthernIrish"},
237: { "age": 22, "gender": "M", "accent": "Scottish"},
363: { "age": 22, "gender": "M", "accent": "Canadian"},
313: { "age": 24, "gender": "F", "accent": "Irish"},
285: { "age": 21, "gender": "M", "accent": "Scottish"},
268: { "age": 23, "gender": "F", "accent": "English"},
302: { "age": 20, "gender": "M", "accent": "Canadian"},
261: { "age": 26, "gender": "F", "accent": "NorthernIrish"},
336: { "age": 18, "gender": "F", "accent": "SouthAfrican"},
288: { "age": 22, "gender": "F", "accent": "Irish"},
226: { "age": 22, "gender": "M", "accent": "English"},
277: { "age": 23, "gender": "F", "accent": "English"},
360: { "age": 19, "gender": "M", "accent": "American"},
257: { "age": 24, "gender": "F", "accent": "English"},
254: { "age": 21, "gender": "M", "accent": "English"},
339: { "age": 21, "gender": "F", "accent": "American"},
323: { "age": 19, "gender": "F", "accent": "SouthAfrican"},
255: { "age": 19, "gender": "M", "accent": "Scottish"},
249: { "age": 22, "gender": "F", "accent": "Scottish"},
293: { "age": 22, "gender": "F", "accent": "NorthernIrish"},
244: { "age": 22, "gender": "F", "accent": "English"},
245: { "age": 25, "gender": "M", "accent": "Irish"},
361: { "age": 19, "gender": "F", "accent": "American"},
314: { "age": 26, "gender": "F", "accent": "SouthAfrican"},
308: { "age": 18, "gender": "F", "accent": "American"},
229: { "age": 23, "gender": "F", "accent": "English"},
341: { "age": 26, "gender": "F", "accent": "American"},
275: { "age": 23, "gender": "M", "accent": "Scottish"},
263: { "age": 22, "gender": "M", "accent": "Scottish"},
253: { "age": 22, "gender": "F", "accent": "Welsh"},
299: { "age": 25, "gender": "F", "accent": "American"},
316: { "age": 20, "gender": "M", "accent": "Canadian"},
282: { "age": 23, "gender": "F", "accent": "English"},
362: { "age": 29, "gender": "F", "accent": "American"},
294: { "age": 33, "gender": "F", "accent": "American"},
274: { "age": 22, "gender": "M", "accent": "English"},
279: { "age": 23, "gender": "M", "accent": "English"},
281: { "age": 29, "gender": "M", "accent": "Scottish"},
286: { "age": 23, "gender": "M", "accent": "English"},
258: { "age": 22, "gender": "M", "accent": "English"},
247: { "age": 22, "gender": "M", "accent": "Scottish"},
351: { "age": 21, "gender": "F", "accent": "NorthernIrish"},
283: { "age": 24, "gender": "F", "accent": "Irish"},
334: { "age": 18, "gender": "M", "accent": "American"},
333: { "age": 19, "gender": "F", "accent": "American"},
295: { "age": 23, "gender": "F", "accent": "Irish"},
330: { "age": 26, "gender": "F", "accent": "American"},
335: { "age": 25, "gender": "F", "accent": "NewZealand"},
228: { "age": 22, "gender": "F", "accent": "English"},
267: { "age": 23, "gender": "F", "accent": "English"},
273: { "age": 18, "gender": "F", "accent": "English"}
}
# Voice model
VOICE_MODEL = "tts_models/en/vctk/vits"
def clean_text(text):
# Remove hyperlinks
return re.sub(r'http[s]?://\S+', '', text)
def extract_paragraphs_from_docx(docx_file):
document = Document(docx_file.name)
paragraphs = [p.text.strip() for p in document.paragraphs if p.text.strip()]
return [clean_text(p) for p in paragraphs]
def list_speaker_choices():
return [f"{sid} | {meta['gender']} | {meta['accent']}" for sid, meta in SPEAKER_METADATA.items()]
def get_speaker_id_from_label(label):
return label.split('|')[0].strip()
# Bark Voice List (Textual Prompts)
bark_voice_choices = [
"young female voice",
"middle-aged male voice with British accent",
"calm narrator",
"excited teenager",
"elderly male voice",
"child with American accent"
]
# Function to generate audio using Coqui TTS (with metadata)
def generate_sample_audio(sample_text, speaker_label, model_choice):
if len(sample_text) > 500:
raise gr.Error("Sample text exceeds 500 characters.")
speaker_id = get_speaker_id_from_label(speaker_label)
if model_choice == "Coqui":
model = TTS("tts_models/multilingual/multi-dataset/your_model")
with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as tmp_wav:
model.tts_to_file(text=sample_text, speaker="p"+speaker_id, file_path=tmp_wav.name)
return tmp_wav.name
elif model_choice == "Bark":
voice_prompt = speaker_label # Bark's speaker prompt could be a descriptive voice label
audio = generate_audio(sample_text, speaker_prompt=voice_prompt) # Bark's method for audio generation
with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as tmp_wav:
audio.export(tmp_wav.name, format="wav")
return tmp_wav.name
else:
model = TTS("tts_models/en/vctk/vits")
with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as tmp_wav:
model.tts_to_file(text=sample_text, speaker="p"+speaker_id, file_path=tmp_wav.name)
return tmp_wav.name
# Function to generate full audio from DOCX using selected TTS model
def generate_audio(docx_file, speaker_label, model_choice):
speaker_id = get_speaker_id_from_label(speaker_label)
if model_choice == "Coqui":
model = TTS("tts_models/multilingual/multi-dataset/your_model")
paragraphs = extract_paragraphs_from_docx(docx_file)
combined_audio = AudioSegment.empty()
temp_files = []
try:
for idx, para in enumerate(paragraphs):
tmp = tempfile.NamedTemporaryFile(suffix=".wav", delete=False)
model.tts_to_file(text=para, speaker="p"+speaker_id, file_path=tmp.name)
audio_chunk = AudioSegment.from_wav(tmp.name)
combined_audio += audio_chunk
temp_files.append(tmp.name)
tmp.close()
except Exception as e:
print("Generation interrupted. Saving partial output.", e)
output_dir = tempfile.mkdtemp()
final_output_path = os.path.join(output_dir, "final_output.wav")
combined_audio.export(final_output_path, format="wav")
zip_path = os.path.join(output_dir, "output.zip")
with zipfile.ZipFile(zip_path, 'w') as zipf:
zipf.write(final_output_path, arcname="final_output.wav")
for f in temp_files:
os.remove(f)
return zip_path
elif model_choice == "Bark":
paragraphs = extract_paragraphs_from_docx(docx_file)
combined_audio = AudioSegment.empty()
try:
for para in paragraphs:
audio = generate_audio(para, speaker_prompt=speaker_label) # Bark
combined_audio += audio # Append audio to final output
except Exception as e:
print("Generation interrupted. Saving partial output.", e)
output_dir = tempfile.mkdtemp()
final_output_path = os.path.join(output_dir, "final_output.wav")
combined_audio.export(final_output_path, format="wav")
zip_path = os.path.join(output_dir, "output.zip")
with zipfile.ZipFile(zip_path, 'w') as zipf:
zipf.write(final_output_path, arcname="final_output.wav")
return zip_path
else: # VCTK
paragraphs = extract_paragraphs_from_docx(docx_file)
combined_audio = AudioSegment.empty()
temp_files = []
try:
for idx, para in enumerate(paragraphs):
tmp = tempfile.NamedTemporaryFile(suffix=".wav", delete=False)
model = TTS("tts_models/en/vctk/vits")
model.tts_to_file(text=para, speaker="p"+speaker_id, file_path=tmp.name)
audio_chunk = AudioSegment.from_wav(tmp.name)
combined_audio += audio_chunk
temp_files.append(tmp.name)
tmp.close()
except Exception as e:
print("Generation interrupted. Saving partial output.", e)
output_dir = tempfile.mkdtemp()
final_output_path = os.path.join(output_dir, "final_output.wav")
combined_audio.export(final_output_path, format="wav")
zip_path = os.path.join(output_dir, "output.zip")
with zipfile.ZipFile(zip_path, 'w') as zipf:
zipf.write(final_output_path, arcname="final_output.wav")
for f in temp_files:
os.remove(f)
return zip_path
# --- UI ---
speaker_choices = list_speaker_choices()
with gr.Blocks() as demo:
gr.Markdown("## 📄 TTS Voice Generator with Paragraph-Wise Processing")
with gr.Row():
model_selector = gr.Dropdown(label="Select TTS Engine", choices=["Coqui", "Bark", "VCTK"], value="VCTK")
speaker_dropdown = gr.Dropdown(label="Select Voice", choices=speaker_choices)
with gr.Row():
sample_textbox = gr.Textbox(label="Enter Sample Text (Max 500 characters)", max_lines=5)
sample_button = gr.Button("Generate Sample")
clear_button = gr.Button("Clear Sample")
sample_audio = gr.Audio(label="Sample Output", type="filepath")
sample_button.click(fn=generate_sample_audio, inputs=[sample_textbox, speaker_dropdown, model_selector], outputs=[sample_audio])
clear_button.click(fn=lambda: None, inputs=[], outputs=[sample_audio])
with gr.Row():
docx_input = gr.File(label="Upload DOCX File", file_types=[".docx"])
generate_button = gr.Button("Generate Full Audio")
download_output = gr.File(label="Download Output Zip")
generate_button.click(fn=generate_audio, inputs=[docx_input, speaker_dropdown, model_selector], outputs=[download_output])
if __name__ == "__main__":
demo.launch()
|