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from typing import Dict, List, Any
import sys, os, re
from tqdm import tqdm
import torch
from transformers import AutoModelForSeq2SeqLM, BitsAndBytesConfig
from IndicTransTokenizer.utils import preprocess_batch, postprocess_batch
from IndicTransTokenizer.tokenizer import IndicTransTokenizer
class EndpointHandler():
def __init__(self, direction = "en-indic", quantization = ""):
self.model_name = "ai4bharat/indictrans2-en-indic-1B"
self.utterance_pattern = re.compile(r"^\d+$")
self.timestamp_pattern = re.compile(r"(\d+:\d+:\d+,\d+)\s*-->\s*(\d+:\d+:\d+,\d+)")
self.BATCH_SIZE = 16
self.DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
self.model = None
self.tokenizer = None
if quantization == "4-bit":
qconfig = BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_use_double_quant=True,
bnb_4bit_compute_dtype=torch.bfloat16,
)
elif quantization == "8-bit":
qconfig = BitsAndBytesConfig(
load_in_8bit=True,
bnb_8bit_use_double_quant=True,
bnb_8bit_compute_dtype=torch.bfloat16,
)
else:
qconfig = None
self.tokenizer = IndicTransTokenizer(direction=direction)
self.model = AutoModelForSeq2SeqLM.from_pretrained(
self.model_name,
trust_remote_code=True,
low_cpu_mem_usage=True,
quantization_config=qconfig
)
if qconfig==None:
self.model = self.model.to(self.DEVICE)
self.model.half()
self.model.eval()
def batch_translate(self, input_sentences, src_lang, tgt_lang):
translations = []
for i in range(0, len(input_sentences), self.BATCH_SIZE):
batch = input_sentences[i : i + self.BATCH_SIZE]
# Preprocess the batch and extract entity mappings
batch, entity_map = preprocess_batch(
batch, src_lang=src_lang, tgt_lang=tgt_lang
)
# Tokenize the batch and generate input encodings
inputs = self.tokenizer(
batch,
src=True,
truncation=True,
padding="longest",
return_tensors="pt",
return_attention_mask=True,
).to(self.DEVICE)
# Generate translations using the model
with torch.no_grad():
generated_tokens = self.model.generate(
**inputs,
use_cache=True,
min_length=0,
max_length=256,
num_beams=5,
num_return_sequences=1,
)
# Decode the generated tokens into text
generated_tokens = self.tokenizer.batch_decode(
generated_tokens.detach().cpu().tolist(), src=False
)
# Postprocess the translations, including entity replacement
translations += postprocess_batch(
generated_tokens, lang=tgt_lang, placeholder_entity_map=entity_map
)
del inputs
if torch.cuda.is_available():
torch.cuda.empty_cache()
return translations
def read_srt(self, srt_path):
data = []
with open(srt_path, 'r', encoding='utf-8') as fp:
utterance_ind = ""
start_end = ""
text = ""
for ind, line in enumerate(fp.readlines()):
line = line.strip()
if re.search(self.utterance_pattern, line) is not None:
utterance_ind = line
elif re.search(self.timestamp_pattern, line) is not None:
start_end = line
else:
text += line
if utterance_ind!='' and start_end!='' and text!='':
data.append({'utterance_ind': utterance_ind, 'start_end': start_end, 'text': text})
utterance_ind = ''
start_end = ''
text = ''
return data
def test(self, inputs) -> List[Dict[str, Any]]:
"""
data args:
inputs (:obj: (transcript_path : 'str', src_lang : 'str', tgt_lang : 'str')
kwargs
Return:
A :obj:`list` | `dict`: will be serialized and returned
"""
src_lang = inputs["src_lang"]
tgt_lang = inputs["tgt_lang"]
transcript_path = inputs["transcript_path"]
output_translations = []
if self.model is not None:
transcriptions = self.read_srt(transcript_path)
trans_sents = [entry['text'] for entry in transcriptions]
indic_translations = self.batch_translate(trans_sents, src_lang, tgt_lang)
for i in tqdm(range(len(transcriptions))):
entry = transcriptions[i]
entry['text'] = indic_translations[i]
output_translations.append(entry)
return output_translations
else:
return []
def __call__(self, data: Dict[str, Any]) -> List[Dict[str, Any]]:
"""
data args:
inputs (:obj: (transcript_path : 'str', src_lang : 'str', tgt_lang : 'str')
kwargs
Return:
A :obj:`list` | `dict`: will be serialized and returned
"""
inputs = data.pop("inputs",data)
src_lang = inputs["src_lang"]
tgt_lang = inputs["tgt_lang"]
transcript_path = inputs["transcript_path"]
output_translations = []
if self.model is not None:
transcriptions = self.read_srt(transcript_path)
trans_sents = [entry['text'] for entry in transcriptions]
indic_translations = self.batch_translate(trans_sents, src_lang, tgt_lang)
for i in tqdm(range(len(transcriptions))):
entry = transcriptions[i]
entry['text'] = indic_translations[i]
output_translations.append(entry)
return output_translations
else:
return [] |