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import argparse
import torch
import os
import json
from tqdm import tqdm
import shortuuid
import whisper
from omni_speech.constants import SPEECH_TOKEN_INDEX, DEFAULT_SPEECH_TOKEN
from omni_speech.conversation import conv_templates, SeparatorStyle
from omni_speech.model.builder import load_pretrained_model, load_pretrained_model_asr
from omni_speech.utils import disable_torch_init
from omni_speech.datasets.preprocess import tokenizer_speech_token
from torch.utils.data import Dataset, DataLoader
import math
def split_list(lst, n):
"""Split a list into n (roughly) equal-sized chunks"""
chunk_size = math.ceil(len(lst) / n) # integer division
return [lst[i:i+chunk_size] for i in range(0, len(lst), chunk_size)]
def get_chunk(lst, n, k):
chunks = split_list(lst, n)
return chunks[k]
# Custom dataset class
class CustomDataset(Dataset):
def __init__(self, questions, tokenizer, model_config, input_type, mel_size, conv_mode, system_prompt):
self.questions = questions
self.tokenizer = tokenizer
self.model_config = model_config
self.input_type = input_type
self.mel_size = mel_size
self.conv_mode = conv_mode
self.system_prompt = system_prompt
def __getitem__(self, index):
item = self.questions[index]
speech_file = item["speech"]
qs = item["conversations"][0]["value"]
conv = conv_templates[self.conv_mode].copy()
conv.append_message(conv.roles[0], qs)
conv.append_message(conv.roles[1], None)
prompt = conv.get_prompt(self.tokenizer, self.system_prompt)
# print(prompt)
# print("----------------")
speech = whisper.load_audio(speech_file)
if self.input_type == "raw":
speech = torch.from_numpy(speech)
if self.model_config.speech_normalize:
speech = torch.nn.functional.layer_norm(speech, speech.shape)
elif self.input_type == "mel":
speech = whisper.pad_or_trim(speech)
speech = whisper.log_mel_spectrogram(speech, n_mels=self.mel_size).permute(1, 0)
input_ids = tokenizer_speech_token(prompt, self.tokenizer, return_tensors='pt')
return input_ids, speech, torch.LongTensor([speech.shape[0]])
def __len__(self):
return len(self.questions)
def collate_fn(batch):
input_ids, speech_tensors, speech_lengths = zip(*batch)
input_ids = torch.stack(input_ids, dim=0)
speech_tensors = torch.stack(speech_tensors, dim=0)
speech_lengths = torch.stack(speech_lengths, dim=0)
return input_ids, speech_tensors, speech_lengths
def ctc_postprocess(tokens, blank):
_toks = tokens.squeeze(0).tolist()
deduplicated_toks = [v for i, v in enumerate(_toks) if i == 0 or v != _toks[i - 1]]
hyp = [v for v in deduplicated_toks if v != blank]
hyp = " ".join(list(map(str, hyp)))
return hyp
# DataLoader
def create_data_loader(questions, tokenizer, model_config, input_type, mel_size, conv_mode, system_prompt, batch_size=1, num_workers=4):
assert batch_size == 1, "batch_size must be 1"
dataset = CustomDataset(questions, tokenizer, model_config, input_type, mel_size, conv_mode, system_prompt)
data_loader = DataLoader(dataset, batch_size=batch_size, num_workers=num_workers, shuffle=False, collate_fn=collate_fn)
return data_loader
def eval_model(args):
# Model
disable_torch_init()
# model_path = os.path.expanduser(args.model_path)
tokenizer, model, context_len = load_pretrained_model(args.model_path, args.model_base, is_lora=args.is_lora, s2s=args.s2s)
questions = json.load(open(os.path.expanduser(args.question_file), "r"))
questions = get_chunk(questions, args.num_chunks, args.chunk_idx)
answers_file = os.path.expanduser(args.answer_file)
os.makedirs(os.path.dirname(answers_file), exist_ok=True)
ans_list = []
data_loader = create_data_loader(questions, tokenizer, model.config, args.input_type, args.mel_size, args.conv_mode, args.system_prompt)
for (input_ids, speech_tensor, speech_length), item in tqdm(zip(data_loader, questions), total=len(questions)):
idx = item["id"]
try:
answer = item["conversations"][1]["value"]
except:
answer = None
input_ids = input_ids.to(device='cuda', non_blocking=True)
speech_tensor = speech_tensor.to(dtype=torch.float16, device='cuda', non_blocking=True)
speech_length = speech_length.to(device='cuda', non_blocking=True)
with torch.inference_mode():
if args.s2s:
outputs = model.generate(
input_ids,
speech=speech_tensor,
speech_lengths=speech_length,
do_sample=True if args.temperature > 0 else False,
temperature=args.temperature,
top_p=args.top_p,
num_beams=args.num_beams,
max_new_tokens=args.max_new_tokens,
use_cache=True,
streaming_unit_gen=False,
)
output_ids, output_units = outputs
else:
outputs = model.generate(
input_ids,
speech=speech_tensor,
speech_lengths=speech_length,
do_sample=True if args.temperature > 0 else False,
temperature=args.temperature,
top_p=args.top_p,
num_beams=args.num_beams,
max_new_tokens=args.max_new_tokens,
use_cache=True,
)
output_ids = outputs
outputs = tokenizer.batch_decode(output_ids, skip_special_tokens=True)[0].strip()
if args.s2s:
output_units = ctc_postprocess(output_units, blank=model.config.unit_vocab_size)
print(f"Preds-{idx}\t{outputs}")
print(f"Ans-{idx}\t{answer}")
if args.s2s:
print(f"U-{idx}\t{output_units}")
if args.s2s:
ans_list.append({"question_id": idx, "prediction": outputs, "prediction_units": output_units, "answer": answer})
# ans_file.write(json.dumps({"question_id": idx, "prediction": outputs, "prediction_units": output_units, "answer": answer}, indent=2) + "\n")
else:
ans_list.append({"question_id": idx, "prediction": outputs, "answer": answer})
# ans_file.write(json.dumps({"question_id": idx, "prediction": outputs, "answer": answer}, indent=2) + "\n")
# ans_file.flush()
with open(answers_file, 'w', encoding='utf-8') as f:
json.dump(ans_list, f, ensure_ascii=False, indent=2)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--model-path", type=str, default="facebook/opt-350m")
parser.add_argument("--model-base", type=str, default=None)
parser.add_argument("--question-file", type=str)
parser.add_argument("--answer-file", type=str)
parser.add_argument("--conv-mode", type=str, default="v1")
parser.add_argument("--num-chunks", type=int, default=1)
parser.add_argument("--chunk-idx", type=int, default=0)
parser.add_argument("--temperature", type=float, default=0)
parser.add_argument("--top_p", type=float, default=None)
parser.add_argument("--num_beams", type=int, default=1)
parser.add_argument("--max_new_tokens", type=int, default=256)
parser.add_argument("--input_type", type=str, default="raw")
parser.add_argument("--mel_size", type=int, default=128)
parser.add_argument("--s2s", action="store_true", default=False)
parser.add_argument("--is_lora", action="store_true", default=False)
parser.add_argument("--system-prompt", type=str, default=None)
args = parser.parse_args()
eval_model(args) |