Spaces:
Build error
Build error
File size: 8,542 Bytes
6bf4ad7 56a498b 6bf4ad7 fa00011 6bf4ad7 fa00011 e8df03b fa00011 6bf4ad7 e8df03b 6bf4ad7 05aebdd 6bf4ad7 56a498b 6bf4ad7 05aebdd 6bf4ad7 |
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 |
from datasets import load_dataset
from transformers import (
DPRQuestionEncoder,
DPRQuestionEncoderTokenizer,
MT5ForConditionalGeneration,
AutoTokenizer,
AutoModelForCTC,
Wav2Vec2Tokenizer,
)
from general_utils import (
embed_questions,
transcript,
remove_chars_to_tts,
parse_final_answer,
)
from typing import List
import gradio as gr
from article_app import article, description, examples
from haystack.nodes import DensePassageRetriever
from haystack.document_stores import InMemoryDocumentStore
import numpy as np
from sentence_transformers import SentenceTransformer, util, CrossEncoder
topk = 21
minchars = 200
min_snippet_length = 20
device = "cpu"
covidterms = ["covid19", "covid", "coronavirus", "covid-19", "sars-cov-2"]
models = {
"wav2vec2-iic": {
"processor": Wav2Vec2Tokenizer.from_pretrained(
"IIC/wav2vec2-spanish-multilibrispeech"
),
"model": AutoModelForCTC.from_pretrained(
"IIC/wav2vec2-spanish-multilibrispeech"
),
},
}
tts_es = gr.Interface.load("huggingface/facebook/tts_transformer-es-css10")
params_generate = {
"min_length": 50,
"max_length": 250,
"do_sample": False,
"early_stopping": True,
"num_beams": 8,
"temperature": 1.0,
"top_k": None,
"top_p": None,
"no_repeat_ngram_size": 3,
"num_return_sequences": 1,
}
dpr = DensePassageRetriever(
document_store=InMemoryDocumentStore(),
query_embedding_model="IIC/dpr-spanish-question_encoder-allqa-base",
passage_embedding_model="IIC/dpr-spanish-passage_encoder-allqa-base",
max_seq_len_query=64,
max_seq_len_passage=256,
batch_size=512,
use_gpu=False,
)
mt5_tokenizer = AutoTokenizer.from_pretrained("IIC/mt5-base-lfqa-es")
mt5_lfqa = MT5ForConditionalGeneration.from_pretrained("IIC/mt5-base-lfqa-es")
similarity_model = SentenceTransformer(
"distiluse-base-multilingual-cased", device="cpu"
)
crossencoder = CrossEncoder("IIC/roberta-base-bne-ranker", device="cpu")
dataset = load_dataset("IIC/spanish_biomedical_crawled_corpus", split="train")
dataset = dataset.filter(lambda example: len(example["text"]) > minchars)
dataset.load_faiss_index(
"embeddings",
"dpr_index_bio_newdpr.faiss",
)
def query_index(question: str):
question_embedding = dpr.embed_queries([question])[0]
scores, closest_passages = dataset.get_nearest_examples(
"embeddings", question_embedding, k=topk
)
contexts = [
closest_passages["text"][i] for i in range(len(closest_passages["text"]))
]
# [:int(topk / 3)]
return [
context for context in contexts if len(context.split()) > min_snippet_length
]
"""
def sort_on_similarity(question, contexts, include_rank: int = 5):
# TODO: METER AQUÍ EL CROSSENCODER nuestro
question_encoded = similarity_model.encode([question])[0]
ctxs_encoded = similarity_model.encode(contexts)
similarity_scores = [
util.cos_sim(question_encoded, ctx_encoded) for ctx_encoded in ctxs_encoded
]
similarity_ranking_idx = np.flip(np.argsort(similarity_scores))
return [contexts[idx] for idx in similarity_ranking_idx][:include_rank]
"""
def sort_on_similarity(question, contexts, include_rank: int = 5):
question_encoded = similarity_model.encode([question])[0]
ctxs_encoded = similarity_model.encode(contexts)
sim_scores_ss = [
util.cos_sim(question_encoded, ctx_encoded) for ctx_encoded in ctxs_encoded
]
text_pairs = [[question, ctx] for ctx in contexts]
similarity_scores = crossencoder.predict(text_pairs)
similarity_scores = np.array(sim_scores_ss) * similarity_scores
similarity_ranking_idx = np.flip(np.argsort(similarity_scores))
return [contexts[idx] for idx in similarity_ranking_idx][:include_rank]
def create_context(contexts: List):
return "<p>" + "<p>".join(contexts)
def create_model_input(question: str, context: str):
return f"question: {question} context: {context}"
def generate_answer(model_input, update_params):
model_input = mt5_tokenizer(
model_input, truncation=True, padding=True, return_tensors="pt", max_length=1024
)
params_generate.update(update_params)
answers_encoded = mt5_lfqa.generate(
input_ids=model_input["input_ids"].to(device),
attention_mask=model_input["attention_mask"].to(device),
**params_generate,
)
answers = mt5_tokenizer.batch_decode(
answers_encoded, skip_special_tokens=True, clean_up_tokenization_spaces=True
)
results = [{"generated_text": answer} for answer in answers]
return results
def search_and_answer(
question,
audio_file,
audio_array,
min_length_answer,
num_beams,
no_repeat_ngram_size,
temperature,
max_answer_length,
wav2vec2_name,
do_tts,
):
update_params = {
"min_length": min_length_answer,
"max_length": max_answer_length,
"num_beams": int(num_beams),
"temperature": temperature,
"no_repeat_ngram_size": no_repeat_ngram_size,
}
if not question:
s2t_model = models[wav2vec2_name]["model"]
s2t_processor = models[wav2vec2_name]["processor"]
question = transcript(
audio_file, audio_array, processor=s2t_processor, model=s2t_model
)
print(f"Transcripted question: *** {question} ****")
if any([any([term in word.lower() for term in covidterms]) for word in question.split(" ")]):
return "Del COVID no queremos saber ya más nada, lo sentimos, pregúntame sobre otra cosa :P ", "ni contexto ni contexta.", "audio_troll.flac"
contexts = query_index(question)
contexts = sort_on_similarity(question, contexts)
context = create_context(contexts)
model_input = create_model_input(question, context)
answers = generate_answer(model_input, update_params)
final_answer = answers[0]["generated_text"]
if do_tts:
audio_answer = tts_es(remove_chars_to_tts(final_answer))
final_answer, documents = parse_final_answer(final_answer, contexts)
return final_answer, documents, audio_answer if do_tts else "audio_troll.flac"
if __name__ == "__main__":
gr.Interface(
search_and_answer,
inputs=[
gr.inputs.Textbox(
lines=2,
label="Question",
placeholder="Type your question (in spanish) to the system.",
optional=True,
),
gr.inputs.Audio(
source="upload",
type="filepath",
label="Upload your audio asking a question here.",
optional=True,
),
gr.inputs.Audio(
source="microphone",
type="numpy",
label="Record your audio asking a question.",
optional=True,
),
gr.inputs.Slider(
minimum=10,
maximum=200,
default=50,
label="Minimum size for the answer",
step=1,
),
gr.inputs.Slider(
minimum=4, maximum=12, default=8, label="number of beams", step=1
),
gr.inputs.Slider(
minimum=2, maximum=5, default=3, label="no repeat n-gram size", step=1
),
gr.inputs.Slider(
minimum=0.8, maximum=2.0, default=1.0, label="temperature", step=0.1
),
gr.inputs.Slider(
minimum=220,
maximum=360,
default=250,
label="maximum answer length",
step=1,
),
gr.inputs.Dropdown(
["wav2vec2-iic"],
type="value",
default=None,
label="Select the speech recognition model.",
optional=False,
),
gr.inputs.Checkbox(
default=False, label="Text to Speech", optional=True),
],
outputs=[
gr.outputs.HTML(
label="Generated Answer."
),
gr.outputs.HTML(
label="Documents used."
),
gr.outputs.Audio(label="Answer in audio"),
],
description=description,
examples=examples,
theme="grass",
article=article,
thumbnail="IIC_logoP.png",
css="https://cdn.jsdelivr.net/npm/[email protected]/dist/css/bootstrap.min.css",
).launch()
|