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import torch
from fairseq.data.text_compressor import TextCompressionLevel, TextCompressor
from fairseq import checkpoint_utils, distributed_utils, options, utils
from fairseq import checkpoint_utils, data, options, tasks
from fairseq.data import FileAudioDataset, AddTargetDataset, Dictionary
from fairseq.tasks.audio_classification import LabelEncoder
import copy
from tqdm import tqdm
import tempfile
import numpy as np
import json
def subset_manifest(infer_manifest, veri_pair):
with open(infer_manifest) as ff, open(veri_pair) as gg, tempfile.NamedTemporaryFile(
"w", delete=False
) as ww:
fnames = ff.read().strip().split("\n")
basedir = fnames[0]
needed_fname = []
for gi in gg.read().strip().split("\n"):
_, x1, x2 = gi.split()
needed_fname.append(x1)
needed_fname.append(x2)
needed_fname = set(needed_fname)
ww.write(basedir + "\n")
for ii in range(1, len(fnames)):
x1, x2 = fnames[ii].split()
if x1 in needed_fname:
ww.write(fnames[ii] + "\n")
print(f"| subset manifest for verification: {ww.name}")
return ww.name
def wrap_target_dataset(infer_manifest, dataset, task):
label_path = infer_manifest.replace(".tsv", ".lang")
text_compressor = TextCompressor(level=TextCompressionLevel.none)
with open(label_path, "r") as f:
labels = [text_compressor.compress(l) for i,l in enumerate(f)]
assert len(labels) == len(dataset)
process_label = LabelEncoder(task.target_dictionary)
dataset = AddTargetDataset(
dataset,
labels,
pad=task.target_dictionary.pad(),
eos=task.target_dictionary.eos(),
batch_targets=True,
process_label=process_label,
add_to_input=False,
)
return dataset
def resample_data(source, padding_mask, n_sample, max_sample_len):
# source: BxT
# padding_mask: BxT
B = source.shape[0]
T = source.shape[1]
sources = []
padding_masks = []
if B == 1:
return [source], [None]
seq_len = (~padding_mask).sum(1)
for jj in range(n_sample):
new_source = source.new_zeros(B, max_sample_len)
new_padding_mask = padding_mask.new_zeros(B, max_sample_len)
for ii in range(B):
if seq_len[ii] > max_sample_len:
start = np.random.randint(0, seq_len[ii] - max_sample_len + 1)
end = start + max_sample_len
else:
start = 0
end = seq_len[ii]
new_source[ii, 0 : end - start] = source[ii, start:end]
new_padding_mask[ii, end - start + 1 :] = True
sources.append(new_source)
padding_masks.append(new_padding_mask)
return sources, padding_masks
def resample_sample(sample, n_sample, max_sample_len):
new_sources, new_padding_masks = resample_data(
sample["net_input"]["source"],
sample["net_input"]["padding_mask"],
n_sample,
max_sample_len,
)
new_samples = []
for ii in range(n_sample):
new_sample = copy.deepcopy(sample)
new_sample["net_input"]["source"] = new_sources[ii]
new_sample["net_input"]["padding_mask"] = new_padding_masks[ii]
new_samples.append(new_sample)
return new_samples
def dict_to_nparr(dd):
dict_class = []
dict_idx = []
for ii, jj in enumerate(dd.symbols):
dict_idx.append(ii)
dict_class.append(jj)
dict_idx = np.array(dict_idx)
dict_class = np.array(dict_class)
return dict_class, dict_idx
if __name__ == "__main__":
np.random.seed(123)
# Parse command-line arguments for generation
parser = options.get_generation_parser(default_task="audio_classification")
# parser.add_argument('--infer-merge', type=str, default='mean')
parser.add_argument("--infer-xtimes", type=int, default=1)
parser.add_argument("--infer-num-samples", type=int, default=None)
parser.add_argument("--top-k", type=int, default=3)
parser.add_argument(
"--infer-max-sample-size", type=int, default=5 * 16000
) # 5 secs
parser.add_argument("--infer-manifest", required=True, type=str)
parser.add_argument("--output-path", default="/tmp/", type=str)
args = options.parse_args_and_arch(parser)
# Setup task
# task = tasks.setup_task(args)
use_cuda = not args.cpu
# Load model & task
print("| loading model from {}".format(args.path))
arg_overrides = {
"task": {
"data": args.data
},
# 'mask_prob': 0
#'max_sample_size': sys.maxsize,
#'min_sample_size': 0,
}
state = checkpoint_utils.load_checkpoint_to_cpu(args.path, arg_overrides)
models, _model_args, task = checkpoint_utils.load_model_ensemble_and_task(
[args.path], arg_overrides=arg_overrides, task=None, state=state
)
model = models[0]
model.eval()
if use_cuda:
model.cuda()
# Load dataset
dict_class, dict_idx = dict_to_nparr(task.target_dictionary)
infer_manifest = args.infer_manifest
infer_dataset = FileAudioDataset(
infer_manifest,
sample_rate=task.cfg.sample_rate,
max_sample_size=10**10, # task.cfg.max_sample_size,
min_sample_size=1, # task.cfg.min_sample_size,
pad=True,
normalize=task.cfg.normalize,
)
# add target (if needed)
infer_dataset = wrap_target_dataset(infer_manifest, infer_dataset, task)
itr = task.get_batch_iterator(
dataset=infer_dataset,
max_sentences=1,
# max_tokens=args.max_tokens,
num_workers=4,
).next_epoch_itr(shuffle=False)
predictions = {}
with torch.no_grad():
for _, sample in tqdm(enumerate(itr)):
# resample if needed
samples = resample_sample(
sample, args.infer_xtimes, args.infer_max_sample_size
)
for sample in samples:
sample = utils.move_to_cuda(sample) if use_cuda else sample
try:
latent = model.forward_latent(**sample["net_input"])
except:
latent = None
logit = model.forward(**sample["net_input"])
logit_lsm = torch.log_softmax(logit.squeeze(), dim=-1)
scores, indices = torch.topk(logit_lsm, args.top_k, dim=-1)
scores = torch.exp(scores).to("cpu").tolist()
indices = indices.to("cpu").tolist()
assert sample["id"].numel() == 1
sample_idx = sample["id"].to("cpu").tolist()[0]
assert sample_idx not in predictions
predictions[sample_idx] = [(task.target_dictionary[int(i)], s) for s, i in zip(scores, indices)]
with open(f"{args.output_path}/predictions.txt", "w") as fo:
for idx in range(len(infer_dataset)):
fo.write(json.dumps(predictions[idx]) + "\n")
print(f"Outputs will be located at - {args.output_path}/predictions.txt")
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