examine + new speak
This commit is contained in:
Gauthier
parent
abf2f2f720
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11201664b8
29
config.json
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29
config.json
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{
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"voices": [
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{
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"name": "Ralph",
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"model_dir": "data/tokens+models/Emerson-Nature.txt_bs=64_ns=8000_vs=5000",
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"tokeniser_file": "data/tokens+models/Emerson-Nature.txt_bs=64_ns=8000_vs=5000/Emerson-Nature.txt_ns=5000.tokenizer.json",
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"temperature": "0.9"
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},
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{
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"name": "Jean",
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"model_dir": "data/tokens+models/Lafontaine-Fables[english].txt_bs=64_ns=8000_vs=5000",
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"tokeniser_file": "data/tokens+models/Lafontaine-Fables[english].txt_bs=64_ns=8000_vs=5000/Lafontaine-Fables[english].txt_ns=5000.tokenizer.json",
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"temperature": "1.2"
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},
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{
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"name": "Blake",
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"model_dir": "data/tokens+models/Blake-Songs-of-Innocence-and-of-Experience.txt_bs=64_ns=8000_vs=5000",
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"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",
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"temperature": "1.5"
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},
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{
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"name": "Friedrich",
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"model_dir": "data/tokens+models/Schelling-ON-THE-RELATION-OF-THE-PLASTIC-ARTS-TO-NATURE.txt_bs=64_ns=8000_vs=5000",
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"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",
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"temperature": "1.5"
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}
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]
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}
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0
examine/__init__.py
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0
examine/__init__.py
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42
examine/doc2vec.py
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42
examine/doc2vec.py
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import pathlib
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import gensim
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TEXTS_DIR = '../data/texts/'
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EXCLUDE = ['Wellcome_TALKS-ABOUT-FLOWERS']
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OUTPUT = '../data/models/doc2vec.model'
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if __name__ == '__main__':
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print("1. Building corpus")
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dirs = [d for d in pathlib.Path(TEXTS_DIR).iterdir() if d.is_dir() and d.stem not in EXCLUDE]
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tagged_corpus = []
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i = 0
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for d in dirs:
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files = list(d.glob('*.txt'))
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for f in files:
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with f.open() as fp:
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text = fp.read()
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tokens = gensim.utils.simple_preprocess(text, max_len=25)
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tag_doc = gensim.models.doc2vec.TaggedDocument(words=tokens, tags=[i])
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tagged_corpus.append(tag_doc)
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i += 1
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print("2. Building vocabulary")
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model = gensim.models.doc2vec.Doc2Vec(vector_size=50, min_count=2, epochs=40)
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model.build_vocab(tagged_corpus)
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print("3. Training")
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model.train(tagged_corpus, total_examples=model.corpus_count, epochs=model.epochs)
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print("4. Saving")
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model.save(OUTPUT)
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18
examine/metric.py
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18
examine/metric.py
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import gensim
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import numpy
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from numpy import dot
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from numpy.linalg import norm
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MODEL_INPUT = '../data/models/doc2vec.model'
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def cos_dist(v0, v1):
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return dot(v0, v1) / (norm(v0) * norm(v1))
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class Metric:
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def __init__(self, model_input=MODEL_INPUT):
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self.model = gensim.models.doc2vec.Doc2Vec.load(model_input)
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def vector(self, text: str):
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tokens = gensim.utils.simple_preprocess(text, max_len=25)
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return self.model.infer_vector(tokens)
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41
speak.py
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speak.py
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import argparse, json, sys, time, random
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import utterance.voice
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import utterance.utils
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def main() -> int:
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p = argparse.ArgumentParser()
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p.add_argument("-c", "--config", type=str, default="config.json", help="configuratin file")
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p.add_argument("-i", "--iterations", type=int, default=10, help="number of iterations")
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args = p.parse_args()
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print(args)
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with open(args.config) as f:
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conf = json.load(f)
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voices = []
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for v in conf['voices']:
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voice = utterance.voice.Voice(name=v["name"].upper(), model=v['model_dir'], tokenizer=v['tokeniser_file'], temp=float(v["temperature"]), lenght=32)
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voices.append(voice)
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nbr_voices = len(voices)
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current_voice = ""
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for i in range(args.iterations):
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rindex = random.randint(0, nbr_voices - 1)
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v = voices[rindex]
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if v.name != current_voice:
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print("==========")
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print(v.name + ":")
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current_voice = v.name
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t = v.utter_one()
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if t != None:
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t = utterance.utils.clean(t)
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t = utterance.utils.format(t)
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print(t)
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time.sleep(4)
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if __name__ == '__main__':
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sys.exit(main())
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117
speak_metric.py
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117
speak_metric.py
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import argparse, json, sys, time, random
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import utterance.voice
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import utterance.utils
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import examine.metric
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def format_str(text: str) -> str:
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t = utterance.utils.clean(text)
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return utterance.utils.format(t)
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def main() -> int:
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p = argparse.ArgumentParser()
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p.add_argument("-c", "--config", type=str, default="config.json", help="configuratin file")
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p.add_argument("-i", "--iterations", type=int, default=10, help="number of iterations")
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args = p.parse_args()
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print(args)
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with open(args.config) as f:
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conf = json.load(f)
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voices = []
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for v in conf['voices']:
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voice = utterance.voice.Voice(name=v["name"].upper(), model=v['model_dir'], tokenizer=v['tokeniser_file'], temp=float(v["temperature"]), lenght=16)
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voices.append(voice)
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nbr_voices = len(voices)
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state = 'c'
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metric = examine.metric.Metric(model_input='data/models/doc2vec.model')
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s = set(range(0, nbr_voices - 1))
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rindex = random.sample(s, 1)[0]
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v = voices[rindex]
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uv = v.utter_one()
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uv = format_str(uv)
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v_vec = metric.vector(uv)
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while state == 'c':
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candidates = random.sample(s, 3)
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results = []
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for c in candidates:
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if c == rindex:
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continue
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vc = voices[c]
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vc_texts = vc.utter_n(n=150)
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for t in vc_texts:
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t = format_str(t)
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t_vec = metric.vector(t)
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d = examine.metric.cos_dist(v_vec, t_vec)
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results.append([d, t, c])
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# vv = voices[rrindex]
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# texts = vv.utter_n(n=150)
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# # texts = v.utter_n(n=150)
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# results = []
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# for t in texts:
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# t = format_str(t)
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# t_vec = metric.vector(uv)
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# d = examine.metric.cos_dist(v_vec, t_vec)
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# results.append((d, t))
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results.sort(key=lambda t: t[0], reverse=True)
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print('----------------------------')
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print(v.name + ":")
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print(uv)
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print('----------------------------')
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for r in results[:2]:
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print('-->' + str(r[0]))
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print(r[1])
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print('+++++++++++++++++++++++++')
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# new round
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top = results[0]
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rindex = top[2]
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v = voices[rindex]
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uv = top[1]
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v_vec = metric.vector(top[1])
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state = input("Continue? ")
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# nbr_voices = len(voices)
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# current_voice = ""
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# for i in range(args.iterations):
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# rindex = random.randint(0, nbr_voices - 1)
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# v = voices[rindex]
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# if v.name != current_voice:
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# print("==========")
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# print(v.name + ":")
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# current_voice = v.name
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# t = v.utter_one()
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# if t != None:
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# t = utterance.utils.clean(t)
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# t = utterance.utils.format(t)
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# print(t)
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# time.sleep(4)
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if __name__ == '__main__':
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sys.exit(main())
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0
utterance/__init__.py
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0
utterance/__init__.py
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@ -1,29 +0,0 @@
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{
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"voices": [
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{
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"name": "Ralph",
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"model_dir": "../data/tokens+models/Emerson-Nature.txt_bs=64_ns=8000_vs=5000",
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"tokeniser_file": "../data/tokens+models/Emerson-Nature.txt_bs=64_ns=8000_vs=5000/Emerson-Nature.txt_ns=5000.tokenizer.json",
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"temperature": "0.9"
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},
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{
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"name": "Jean",
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"model_dir": "../data/tokens+models/Lafontaine-Fables[english].txt_bs=64_ns=8000_vs=5000",
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"tokeniser_file": "../data/tokens+models/Lafontaine-Fables[english].txt_bs=64_ns=8000_vs=5000/Lafontaine-Fables[english].txt_ns=5000.tokenizer.json",
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"temperature": "1.2"
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},
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{
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"name": "Blake",
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"model_dir": "../data/tokens+models/Blake-Songs-of-Innocence-and-of-Experience.txt_bs=64_ns=8000_vs=5000",
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"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",
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"temperature": "1.5"
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},
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{
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"name": "Friedrich",
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"model_dir": "../data/tokens+models/Schelling-ON-THE-RELATION-OF-THE-PLASTIC-ARTS-TO-NATURE.txt_bs=64_ns=8000_vs=5000",
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"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",
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"temperature": "1.5"
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}
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]
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}
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import argparse, json, sys, time, random
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import spacy
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from aitextgen import aitextgen
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import string
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def clean(text: str) -> str:
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s = text.split('\n')
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if(len(s) > 0):
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tok_1 = s[0].split(' ')
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if len(tok_1) > 0 and tok_1[0].strip() in string.punctuation:
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s_1 = ' '.join(tok_1[1:])
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s[0] = s_1.capitalize()
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else:
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s[0] = s[0].capitalize()
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return '\n'.join(s)
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def format(text: str) -> str:
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return text.replace('\r\n', '\n').replace('\n\n', '\n')
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def main() -> int:
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p = argparse.ArgumentParser()
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p.add_argument("-c", "--config", type=str, default="config.json", help="configuratin file")
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p.add_argument("-i", "--iterations", type=int, default=10, help="number of iterations")
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args = p.parse_args()
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print(args)
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with open(args.config) as f:
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conf = json.load(f)
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voices = []
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for v in conf['voices']:
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a = aitextgen(model_folder=v['model_dir'], tokenizer_file=v['tokeniser_file'])
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voices.append({"name": v["name"].upper(), "a": a, "temp": float(v["temperature"])})
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nbr_voices = len(voices)
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current_voice = ""
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for i in range(args.iterations):
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rindex = random.randint(0, nbr_voices - 1)
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v = voices[rindex]
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if v['name'] != current_voice:
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print("==========")
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print(v['name'] + ":")
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current_voice = v['name']
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t = v['a'].generate(n=1, max_lenght=32, temperature=v['temp'], return_as_list=True)[0]
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if t != None:
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t = clean(t)
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t = format(t)
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print(t)
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time.sleep(4)
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if __name__ == '__main__':
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sys.exit(main())
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19
utterance/utils.py
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19
utterance/utils.py
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import string
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def clean(text: str) -> str:
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s = text.split('\n')
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if(len(s) > 0):
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tok_1 = s[0].split(' ')
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if len(tok_1) > 0 and tok_1[0].strip() in string.punctuation:
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s_1 = ' '.join(tok_1[1:])
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s[0] = s_1.capitalize()
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else:
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s[0] = s[0].capitalize()
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return '\n'.join(s)
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def format(text: str) -> str:
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return text.replace('\r\n', '\n').replace('\n\n', '\n')
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18
utterance/voice.py
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18
utterance/voice.py
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from aitextgen import aitextgen
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import utterance.utils
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class Voice:
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def __init__(self, name: str, model: str, tokenizer: str, temp: int, lenght: int):
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self.name = name
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self.temp = temp
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self.lenght = lenght
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self.v = aitextgen(model_folder=model, tokenizer_file=tokenizer)
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def utter_n(self, n: int, temp: float = None, lenght: int = None):
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t = self.temp if temp != None else temp
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l = self.lenght if lenght != None else lenght
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return self.v.generate(n=n, max_lenght=l, temperature=t, return_as_list=True)
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def utter_one(self, temp: int = None, lenght: float = None) -> str:
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return self.utter_n(n=1, temp=temp, lenght=lenght)[0]
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