File size: 14,320 Bytes
d19665e | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 | import os
import re
import json
import torch
import inflect
import random
import uroman as ur
import numpy as np
import torchaudio
from transformers import AutoTokenizer
from outetts.wav_tokenizer.decoder import WavTokenizer
from outetts.wav_tokenizer.encoder.utils import convert_audio
class AudioTokenizer:
def __init__(self,tokenizer_path,wav_tokenizer_model_path,wav_tokenizer_config_path,):
self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
self.text_prompt = "{bos}\n{text_start}{words}{text_end}\n{audio_start}\n"
self.tokenizer = AutoTokenizer.from_pretrained(tokenizer_path)
self.bos = "<|im_start|>"
self.eos = "<|im_end|>"
self.input_length=0
self.special_tokens = {
"audio_code": "<|{}|>",
"text_start": "<|text_start|>",
"text_end": "<|text_end|>",
"audio_start": "<|audio_start|>",
"audio_end": "<|audio_end|>",
"time": "<|t_{:.2f}|>",
"code_start": "<|code_start|>",
"code_end": "<|code_end|>",
"text_sep": "<|text_sep|>"
}
self.lec = inflect.engine()
#self.text_prompt = "{bos}\n{text_start}{words}{text_end}\n{audio_start}\n"
#self.config_path = "/content/wavtokenizer_mediumdata_frame75_3s_nq1_code4096_dim512_kmeans200_attn.yaml"
#self.model_path = "/content/wavtokenizer_large_speech_320_24k.ckpt"
self.wavtokenizer = WavTokenizer.from_pretrained0802(wav_tokenizer_config_path, wav_tokenizer_model_path)
self.wavtokenizer = self.wavtokenizer.to(self.device)
self.BASE_DIR = os.path.dirname(__file__)
self.DEFAULT_SPEAKERS_DIR = os.path.join(self.BASE_DIR, "default_speakers")
self.speakers=["idera","emma","onye","jude","osagie","tayo","zainab","joke","regina","remi","umar","chinenye"]
def get_speaker_path(self,speaker_name):
return os.path.join(self.DEFAULT_SPEAKERS_DIR, f"{speaker_name}.json")
def load_speaker(self, path: str):
with open(path, "r") as f:
return json.load(f)
def load_default_speaker(self, name: str):
name = name.lower().strip()
speaker_path=self.get_speaker_path(name)
return self.load_speaker(speaker_path)
def process_text(self, text: str):
text = re.sub(r'\d+(\.\d+)?', lambda x: self.lec.number_to_words(x.group()), text.lower())
text = re.sub(r'[-_/,\.\\]', ' ', text)
text = re.sub(r'[^a-z\s]', '', text)
text = re.sub(r'\s+', ' ', text).strip()
return text.split()
def create_audio_prompt(self,words: list) -> str:
prompt = []
for i in words:
word = i["word"]
duration = self.special_tokens["time"].format(float(i["duration"]))
tokens = "".join([self.special_tokens["audio_code"].format(c) for c in i["codes"]])
prompt.append(f'{word}{duration}{self.special_tokens["code_start"]}{tokens}{self.special_tokens["code_end"]}')
return "\n".join(prompt)
def create_prompt(self,text,speaker_name="idera"):
speaker=self.load_default_speaker(speaker_name)
input_words = self.process_text(speaker["text"]) + self.process_text(text)
#input_words = process_text(speaker["text"]) + input_words
inputs_words_strings = f"{self.special_tokens['text_sep']}".join([i.strip() for i in input_words])
prompt = self.text_prompt.format(
bos=self.bos,
text_start=self.special_tokens['text_start'],
words=inputs_words_strings,
text_end=self.special_tokens['text_end'],
audio_start=self.special_tokens['audio_start']
)
prompt += self.create_audio_prompt(speaker["words"])
return prompt
def tokenize_prompt(self, prompt):
input_ids = self.tokenizer.encode(
prompt,
add_special_tokens=False,
return_tensors="pt"
).to(self.device)
self.input_length=input_ids.shape[1]
return input_ids.to(self.device)
def get_audio(self,discrete_code):
discrete_code=torch.tensor([[discrete_code]]).to(self.device)
features = self.wavtokenizer.codes_to_features(discrete_code).to(self.device)
bandwidth_id = torch.tensor([0]).to(self.device)
audio_out = self.wavtokenizer.decode(features, bandwidth_id=bandwidth_id)
return audio_out.to("cpu")
def extract_integers(self,s):
# Match integers enclosed in vertical bars |integer|
matches = re.findall(r'\|(-?\d+)\|', s)
# Convert matches to integers
return [int(match) for match in matches]
def get_codes(self, output):
new_output=self.tokenizer.decode(output[0][self.input_length:])
codes=self.extract_integers(new_output)
return codes
class AudioTokenizerForLocal(AudioTokenizer):
def __init__(self,tokenizer_path,wav_tokenizer_model_path,wav_tokenizer_config_path,):
super().__init__(tokenizer_path, wav_tokenizer_model_path, wav_tokenizer_config_path)
self.text_prompt = "{bos}\n{text_start}{words}{text_end}\n{lang}\n{audio_start}\n"
self.special_tokens = {
"audio_code": "<|{}|>",
"text_start": "<|text_start|>",
"text_end": "<|text_end|>",
"audio_start": "<|audio_start|>",
"audio_end": "<|audio_end|>",
"word_start": "<|word_start|>",
"word_end": "<|word_end|>",
"time": "<|t_{:.2f}|>",
"code_start": "<|code_start|>",
"code_end": "<|code_end|>",
"text_sep": "<|text_sep|>",
"hausa":"<|hausa|>",
"igbo":"<|igbo|>",
"yoruba":"<|yoruba|>",
}
self.uroman = ur.Uroman()
self.DEFAULT_SPEAKERS_DIR = os.path.join(self.BASE_DIR, "default_speakers_local")
self.speakers = [
"hausa_male1", "hausa_male2","yoruba_male1", "yoruba_male2","igbo_male2" #"igbo_male1", "igbo_male2",
"hausa_female1", "hausa_female2", "igbo_female1", "igbo_female2", "yoruba_female1", "yoruba_female2"
]
def process_text(self, text: str):
text = self.uroman.romanize_string(text)
text = re.sub(r'\d+(\.\d+)?', lambda x: self.lec.number_to_words(x.group()), text.lower())
text = re.sub(r'[-_/,\.\\]', ' ', text)
text = re.sub(r'[^a-z\s]', '', text)
text = re.sub(r'\s+', ' ', text).strip()
return text.split()
def create_prompt(self,text,lang,speaker_name=None):
assert lang in ["hausa","igbo","yoruba"], f"Invalid language: {lang}, language must be one of ['hausa','igbo','yoruba']"
#if no speaker
if speaker_name is None:
if lang=="hausa":
speaker_name=random.choice(["hausa_male1","hausa_male2","hausa_female1","hausa_female2"])
elif lang=="igbo":
speaker_name=random.choice(["igbo_female1","igbo_female2","igbo_male2"])#"igbo_male1"])
else:
speaker_name=random.choice(["yoruba_male2","yoruba_female1","yoruba_female2"])
speaker=self.load_default_speaker(speaker_name)
input_words = self.process_text(speaker["text"]) + self.process_text(text)
#input_words = process_text(speaker["text"]) + input_words
inputs_words_strings = f"{self.special_tokens['text_sep']}".join([i.strip() for i in input_words])
prompt = self.text_prompt.format(
bos=self.bos,
text_start=self.special_tokens['text_start'],
words=inputs_words_strings,
text_end=self.special_tokens['text_end'],
lang=self.special_tokens[lang],
audio_start=self.special_tokens['audio_start']
)
prompt += self.create_audio_prompt(speaker["words"])
return prompt
class AudioTokenizerV2(AudioTokenizer):
def __init__(self,tokenizer_path,wav_tokenizer_model_path,wav_tokenizer_config_path,):
super().__init__(tokenizer_path, wav_tokenizer_model_path, wav_tokenizer_config_path)
self.text_prompt = "{bos}\n{text_start}{words}{text_end}\n{lang}\n{audio_start}\n"
self.asr_prompt="{bos}\n{code_start}{codes}{code_end}\n{asr}\n"
self.special_tokens = {
"audio_code": "<|{}|>",
"text_start": "<|text_start|>",
"text_end": "<|text_end|>",
"audio_start": "<|audio_start|>",
"audio_end": "<|audio_end|>",
"word_start": "<|word_start|>",
"word_end": "<|word_end|>",
"time": "<|t_{:.2f}|>",
"code_start": "<|code_start|>",
"code_end": "<|code_end|>",
"text_sep": "<|text_sep|>",
"hausa":"<|hausa|>",
"igbo":"<|igbo|>",
"yoruba":"<|yoruba|>",
"english":"<|english|>",#<|english|>
"asr":"<|asr|>"
}
self.uroman = ur.Uroman()
self.DEFAULT_SPEAKERS_DIR_LOCAL = os.path.join(self.BASE_DIR, "default_speakers_local")
self.DEFAULT_SPEAKERS_ENG = os.path.join(self.BASE_DIR, "default_speakers")
self.speakers_local = [
"hausa_male1", "hausa_male2","yoruba_male1", "yoruba_male2","igbo_male2" #"igbo_male1", "igbo_male2",
"hausa_female1", "hausa_female2", "igbo_female1", "igbo_female2", "yoruba_female1", "yoruba_female2"
]
self.speakers_eng = ["idera","emma","onye","jude","osagie","tayo","zainab","joke","regina","remi","umar","chinenye","saheed"]
self.changed_tokens=[('<|1836|>', '<|453|><|453|>'),
('<|1837|>', '<|1836|><|1836|>'),
('<|1838|>', '<|1837|><|1837|>'),
('<|1840|>', '<|244|><|167|>'),
('<|1841|>', '<|235|><|219|>'),
('<|1844|>', '<|453|><|244|>'),
('<|1845|>', '<|1838|><|1838|>')]
def process_text(self, text: str):
text = self.uroman.romanize_string(text)
text = re.sub(r'\d+(\.\d+)?', lambda x: self.lec.number_to_words(x.group()), text.lower())
text = re.sub(r'[-_/,\.\\]', ' ', text)
text = re.sub(r'[^a-z\s]', '', text)
text = re.sub(r'\s+', ' ', text).strip()
return text.split()
def get_speaker_path(self,speaker_name,dir):
return os.path.join(dir, f"{speaker_name}.json")
def load_speaker(self, path: str):
with open(path, "r") as f:
return json.load(f)
def load_default_speaker(self, name: str,dir: str):
name = name.lower().strip()
speaker_path=self.get_speaker_path(name,dir)
return self.load_speaker(speaker_path)
def create_prompt(self,text,lang,speaker_name=None):
assert lang in ["hausa","igbo","yoruba","english"], f"Invalid language: {lang}, language must be one of ['hausa','igbo','yoruba','english']"
#if no speaker
dir=self.DEFAULT_SPEAKERS_DIR_LOCAL
if speaker_name is None:
if lang=="hausa":
speaker_name=random.choice(["hausa_male1","hausa_male2","hausa_female1","hausa_female2"])
elif lang=="igbo":
speaker_name=random.choice(["igbo_female1","igbo_female2","igbo_male2"])#"igbo_male1"])
elif lang=="yoruba":
speaker_name=random.choice(["yoruba_male2","yoruba_female1","yoruba_female2"])
else:
speaker_name=random.choice(self.speakers_eng)
if lang=="english":
dir=self.DEFAULT_SPEAKERS_ENG
speaker=self.load_default_speaker(speaker_name,dir)
input_words = self.process_text(speaker["text"]) + self.process_text(text)
#input_words = process_text(speaker["text"]) + input_words
inputs_words_strings = f"{self.special_tokens['text_sep']}".join([i.strip() for i in input_words])
prompt = self.text_prompt.format(
bos=self.bos,
text_start=self.special_tokens['text_start'],
words=inputs_words_strings,
text_end=self.special_tokens['text_end'],
lang=self.special_tokens[lang],
audio_start=self.special_tokens['audio_start']
)
prompt += self.create_audio_prompt(speaker["words"])
return prompt
def replace_tokens(text):
for pair in self.changed_tokens:
text=text.replace(pair[0],pair[-1])
return text
def resample(self,audio: np.ndarray, sr: int, target_sr: int):
audio = audio.to(dtype=torch.float32)
#.clone().detach()
audio = audio.unsqueeze(0)
# 1 as last arg corresponds to mono audio
resampled = convert_audio(audio, sr, target_sr, 1)
return resampled.to(self.device )
def quantize_wavtokenizer(self, path):
audio_data, sample_rate = torchaudio.load(path)
audio_data=audio_data.squeeze()
audio = self.resample(audio_data, sample_rate, 24000).to(self.device)
bandwidth_id = torch.tensor([0]).to(self.device )
_, codes = self.wavtokenizer.encode_infer(audio, bandwidth_id=bandwidth_id)
codes = codes.squeeze(1).to(self.device)#+last_text_token
res=""
for code in codes[0].tolist():
res+=f"<|{code}|>"
return res
def load_asr_prompt(self,audio_path):
codes=self.quantize_wavtokenizer(audio_path)
prompt = self.asr_prompt.format(
bos=self.bos,
code_start=self.special_tokens['code_start'],
codes=codes,
code_end=self.special_tokens['code_end'],
asr=self.special_tokens["asr"],
)
return prompt
def get_asr_results(self,output):
res=""
for text in self.tokenizer.decode(output[0]).split("<|text_start|>")[-1].split("<|text_end|>")[0].split("\n"):
res+=text.split("<|word_start|>")[-1].split("<|word_end|>")[0]
res+=" "
return res.strip() |