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examine + new speak

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Gauthier 2022-03-06 14:20:53 +01:00
parent abf2f2f720
commit 11201664b8
13 changed files with 284 additions and 89 deletions
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29
config.json Normal file
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{
"voices": [
{
"name": "Ralph",
"model_dir": "data/tokens+models/Emerson-Nature.txt_bs=64_ns=8000_vs=5000",
"tokeniser_file": "data/tokens+models/Emerson-Nature.txt_bs=64_ns=8000_vs=5000/Emerson-Nature.txt_ns=5000.tokenizer.json",
"temperature": "0.9"
},
{
"name": "Jean",
"model_dir": "data/tokens+models/Lafontaine-Fables[english].txt_bs=64_ns=8000_vs=5000",
"tokeniser_file": "data/tokens+models/Lafontaine-Fables[english].txt_bs=64_ns=8000_vs=5000/Lafontaine-Fables[english].txt_ns=5000.tokenizer.json",
"temperature": "1.2"
},
{
"name": "Blake",
"model_dir": "data/tokens+models/Blake-Songs-of-Innocence-and-of-Experience.txt_bs=64_ns=8000_vs=5000",
"tokeniser_file": "data/tokens+models/Blake-Songs-of-Innocence-and-of-Experience.txt_bs=64_ns=8000_vs=5000/Blake-Songs-of-Innocence-and-of-Experience.txt_ns=5000.tokenizer.json",
"temperature": "1.5"
},
{
"name": "Friedrich",
"model_dir": "data/tokens+models/Schelling-ON-THE-RELATION-OF-THE-PLASTIC-ARTS-TO-NATURE.txt_bs=64_ns=8000_vs=5000",
"tokeniser_file": "data/tokens+models/Schelling-ON-THE-RELATION-OF-THE-PLASTIC-ARTS-TO-NATURE.txt_bs=64_ns=8000_vs=5000/Schelling-ON-THE-RELATION-OF-THE-PLASTIC-ARTS-TO-NATURE.txt_ns=5000.tokenizer.json",
"temperature": "1.5"
}
]
}

0
examine/__init__.py Normal file
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42
examine/doc2vec.py Normal file
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import pathlib
import gensim
TEXTS_DIR = '../data/texts/'
EXCLUDE = ['Wellcome_TALKS-ABOUT-FLOWERS']
OUTPUT = '../data/models/doc2vec.model'
if __name__ == '__main__':
print("1. Building corpus")
dirs = [d for d in pathlib.Path(TEXTS_DIR).iterdir() if d.is_dir() and d.stem not in EXCLUDE]
tagged_corpus = []
i = 0
for d in dirs:
files = list(d.glob('*.txt'))
for f in files:
with f.open() as fp:
text = fp.read()
tokens = gensim.utils.simple_preprocess(text, max_len=25)
tag_doc = gensim.models.doc2vec.TaggedDocument(words=tokens, tags=[i])
tagged_corpus.append(tag_doc)
i += 1
print("2. Building vocabulary")
model = gensim.models.doc2vec.Doc2Vec(vector_size=50, min_count=2, epochs=40)
model.build_vocab(tagged_corpus)
print("3. Training")
model.train(tagged_corpus, total_examples=model.corpus_count, epochs=model.epochs)
print("4. Saving")
model.save(OUTPUT)

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examine/metric.py Normal file
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import gensim
import numpy
from numpy import dot
from numpy.linalg import norm
MODEL_INPUT = '../data/models/doc2vec.model'
def cos_dist(v0, v1):
return dot(v0, v1) / (norm(v0) * norm(v1))
class Metric:
def __init__(self, model_input=MODEL_INPUT):
self.model = gensim.models.doc2vec.Doc2Vec.load(model_input)
def vector(self, text: str):
tokens = gensim.utils.simple_preprocess(text, max_len=25)
return self.model.infer_vector(tokens)

41
speak.py Normal file
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import argparse, json, sys, time, random
import utterance.voice
import utterance.utils
def main() -> int:
p = argparse.ArgumentParser()
p.add_argument("-c", "--config", type=str, default="config.json", help="configuratin file")
p.add_argument("-i", "--iterations", type=int, default=10, help="number of iterations")
args = p.parse_args()
print(args)
with open(args.config) as f:
conf = json.load(f)
voices = []
for v in conf['voices']:
voice = utterance.voice.Voice(name=v["name"].upper(), model=v['model_dir'], tokenizer=v['tokeniser_file'], temp=float(v["temperature"]), lenght=32)
voices.append(voice)
nbr_voices = len(voices)
current_voice = ""
for i in range(args.iterations):
rindex = random.randint(0, nbr_voices - 1)
v = voices[rindex]
if v.name != current_voice:
print("==========")
print(v.name + ":")
current_voice = v.name
t = v.utter_one()
if t != None:
t = utterance.utils.clean(t)
t = utterance.utils.format(t)
print(t)
time.sleep(4)
if __name__ == '__main__':
sys.exit(main())

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speak_metric.py Normal file
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import argparse, json, sys, time, random
import utterance.voice
import utterance.utils
import examine.metric
def format_str(text: str) -> str:
t = utterance.utils.clean(text)
return utterance.utils.format(t)
def main() -> int:
p = argparse.ArgumentParser()
p.add_argument("-c", "--config", type=str, default="config.json", help="configuratin file")
p.add_argument("-i", "--iterations", type=int, default=10, help="number of iterations")
args = p.parse_args()
print(args)
with open(args.config) as f:
conf = json.load(f)
voices = []
for v in conf['voices']:
voice = utterance.voice.Voice(name=v["name"].upper(), model=v['model_dir'], tokenizer=v['tokeniser_file'], temp=float(v["temperature"]), lenght=16)
voices.append(voice)
nbr_voices = len(voices)
state = 'c'
metric = examine.metric.Metric(model_input='data/models/doc2vec.model')
s = set(range(0, nbr_voices - 1))
rindex = random.sample(s, 1)[0]
v = voices[rindex]
uv = v.utter_one()
uv = format_str(uv)
v_vec = metric.vector(uv)
while state == 'c':
candidates = random.sample(s, 3)
results = []
for c in candidates:
if c == rindex:
continue
vc = voices[c]
vc_texts = vc.utter_n(n=150)
for t in vc_texts:
t = format_str(t)
t_vec = metric.vector(t)
d = examine.metric.cos_dist(v_vec, t_vec)
results.append([d, t, c])
# vv = voices[rrindex]
# texts = vv.utter_n(n=150)
# # texts = v.utter_n(n=150)
# results = []
# for t in texts:
# t = format_str(t)
# t_vec = metric.vector(uv)
# d = examine.metric.cos_dist(v_vec, t_vec)
# results.append((d, t))
results.sort(key=lambda t: t[0], reverse=True)
print('----------------------------')
print(v.name + ":")
print(uv)
print('----------------------------')
for r in results[:2]:
print('-->' + str(r[0]))
print(r[1])
print('+++++++++++++++++++++++++')
# new round
top = results[0]
rindex = top[2]
v = voices[rindex]
uv = top[1]
v_vec = metric.vector(top[1])
state = input("Continue? ")
# nbr_voices = len(voices)
# current_voice = ""
# for i in range(args.iterations):
# rindex = random.randint(0, nbr_voices - 1)
# v = voices[rindex]
# if v.name != current_voice:
# print("==========")
# print(v.name + ":")
# current_voice = v.name
# t = v.utter_one()
# if t != None:
# t = utterance.utils.clean(t)
# t = utterance.utils.format(t)
# print(t)
# time.sleep(4)
if __name__ == '__main__':
sys.exit(main())

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tokenise.py → train/tokenise.py
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0
train.py → train/train.py
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0
utterance/__init__.py Normal file
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29
utterance/config.json
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{
"voices": [
{
"name": "Ralph",
"model_dir": "../data/tokens+models/Emerson-Nature.txt_bs=64_ns=8000_vs=5000",
"tokeniser_file": "../data/tokens+models/Emerson-Nature.txt_bs=64_ns=8000_vs=5000/Emerson-Nature.txt_ns=5000.tokenizer.json",
"temperature": "0.9"
},
{
"name": "Jean",
"model_dir": "../data/tokens+models/Lafontaine-Fables[english].txt_bs=64_ns=8000_vs=5000",
"tokeniser_file": "../data/tokens+models/Lafontaine-Fables[english].txt_bs=64_ns=8000_vs=5000/Lafontaine-Fables[english].txt_ns=5000.tokenizer.json",
"temperature": "1.2"
},
{
"name": "Blake",
"model_dir": "../data/tokens+models/Blake-Songs-of-Innocence-and-of-Experience.txt_bs=64_ns=8000_vs=5000",
"tokeniser_file": "../data/tokens+models/Blake-Songs-of-Innocence-and-of-Experience.txt_bs=64_ns=8000_vs=5000/Blake-Songs-of-Innocence-and-of-Experience.txt_ns=5000.tokenizer.json",
"temperature": "1.5"
},
{
"name": "Friedrich",
"model_dir": "../data/tokens+models/Schelling-ON-THE-RELATION-OF-THE-PLASTIC-ARTS-TO-NATURE.txt_bs=64_ns=8000_vs=5000",
"tokeniser_file": "../data/tokens+models/Schelling-ON-THE-RELATION-OF-THE-PLASTIC-ARTS-TO-NATURE.txt_bs=64_ns=8000_vs=5000/Schelling-ON-THE-RELATION-OF-THE-PLASTIC-ARTS-TO-NATURE.txt_ns=5000.tokenizer.json",
"temperature": "1.5"
}
]
}

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utterance/speak.py
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import argparse, json, sys, time, random
import spacy
from aitextgen import aitextgen
import string
def clean(text: str) -> str:
s = text.split('\n')
if(len(s) > 0):
tok_1 = s[0].split(' ')
if len(tok_1) > 0 and tok_1[0].strip() in string.punctuation:
s_1 = ' '.join(tok_1[1:])
s[0] = s_1.capitalize()
else:
s[0] = s[0].capitalize()
return '\n'.join(s)
def format(text: str) -> str:
return text.replace('\r\n', '\n').replace('\n\n', '\n')
def main() -> int:
p = argparse.ArgumentParser()
p.add_argument("-c", "--config", type=str, default="config.json", help="configuratin file")
p.add_argument("-i", "--iterations", type=int, default=10, help="number of iterations")
args = p.parse_args()
print(args)
with open(args.config) as f:
conf = json.load(f)
voices = []
for v in conf['voices']:
a = aitextgen(model_folder=v['model_dir'], tokenizer_file=v['tokeniser_file'])
voices.append({"name": v["name"].upper(), "a": a, "temp": float(v["temperature"])})
nbr_voices = len(voices)
current_voice = ""
for i in range(args.iterations):
rindex = random.randint(0, nbr_voices - 1)
v = voices[rindex]
if v['name'] != current_voice:
print("==========")
print(v['name'] + ":")
current_voice = v['name']
t = v['a'].generate(n=1, max_lenght=32, temperature=v['temp'], return_as_list=True)[0]
if t != None:
t = clean(t)
t = format(t)
print(t)
time.sleep(4)
if __name__ == '__main__':
sys.exit(main())

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utterance/utils.py Normal file
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import string
def clean(text: str) -> str:
s = text.split('\n')
if(len(s) > 0):
tok_1 = s[0].split(' ')
if len(tok_1) > 0 and tok_1[0].strip() in string.punctuation:
s_1 = ' '.join(tok_1[1:])
s[0] = s_1.capitalize()
else:
s[0] = s[0].capitalize()
return '\n'.join(s)
def format(text: str) -> str:
return text.replace('\r\n', '\n').replace('\n\n', '\n')

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utterance/voice.py Normal file
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from aitextgen import aitextgen
import utterance.utils
class Voice:
def __init__(self, name: str, model: str, tokenizer: str, temp: int, lenght: int):
self.name = name
self.temp = temp
self.lenght = lenght
self.v = aitextgen(model_folder=model, tokenizer_file=tokenizer)
def utter_n(self, n: int, temp: float = None, lenght: int = None):
t = self.temp if temp != None else temp
l = self.lenght if lenght != None else lenght
return self.v.generate(n=n, max_lenght=l, temperature=t, return_as_list=True)
def utter_one(self, temp: int = None, lenght: float = None) -> str:
return self.utter_n(n=1, temp=temp, lenght=lenght)[0]