def inference(
output_dir: str,
batch_size: int,
dtype: str,
fs: int,
ngpu: int,
seed: int,
num_workers: int,
log_level: Union[int, str],
data_path_and_name_and_type: Sequence[Tuple[str, str, str]],
key_file: Optional[str],
train_config: Optional[str],
model_file: Optional[str],
model_tag: Optional[str],
inference_config: Optional[str],
allow_variable_data_keys: bool,
segment_size: Optional[float],
hop_size: Optional[float],
normalize_segment_scale: bool,
show_progressbar: bool,
ref_channel: Optional[int],
normalize_output_wav: bool,
enh_s2t_task: bool,
):
assert check_argument_types()
if batch_size > 1:
raise NotImplementedError("batch decoding is not implemented")
if ngpu > 1:
raise NotImplementedError("only single GPU decoding is supported")
logging.basicConfig(
level=log_level,
format="%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s",
)
if ngpu >= 1:
device = "cuda"
else:
device = "cpu"
# 1. Set random-seed
set_all_random_seed(seed)
# 2. Build separate_speech
separate_speech_kwargs = dict(
train_config=train_config,
model_file=model_file,
inference_config=inference_config,
segment_size=segment_size,
hop_size=hop_size,
normalize_segment_scale=normalize_segment_scale,
show_progressbar=show_progressbar,
ref_channel=ref_channel,
normalize_output_wav=normalize_output_wav,
device=device,
dtype=dtype,
enh_s2t_task=enh_s2t_task,
)
separate_speech = SeparateSpeech.from_pretrained(
model_tag=model_tag,
**separate_speech_kwargs,
)
# 3. Build data-iterator
loader = EnhancementTask.build_streaming_iterator(
data_path_and_name_and_type,
dtype=dtype,
batch_size=batch_size,
key_file=key_file,
num_workers=num_workers,
preprocess_fn=EnhancementTask.build_preprocess_fn(
separate_speech.enh_train_args, False),
collate_fn=EnhancementTask.build_collate_fn(
separate_speech.enh_train_args, False),
allow_variable_data_keys=allow_variable_data_keys,
inference=True,
)
# 4. Start for-loop
output_dir = Path(output_dir).expanduser().resolve()
writers = []
for i in range(separate_speech.num_spk):
writers.append(
SoundScpWriter(f"{output_dir}/wavs/{i + 1}",
f"{output_dir}/spk{i + 1}.scp"))
for i, (keys, batch) in enumerate(loader):
logging.info(f"[{i}] Enhancing {keys}")
assert isinstance(batch, dict), type(batch)
assert all(isinstance(s, str) for s in keys), keys
_bs = len(next(iter(batch.values())))
assert len(keys) == _bs, f"{len(keys)} != {_bs}"
batch = {k: v for k, v in batch.items() if not k.endswith("_lengths")}
waves = separate_speech(**batch)
for (spk, w) in enumerate(waves):
for b in range(batch_size):
writers[spk][keys[b]] = fs, w[b]
for writer in writers:
writer.close()
def inference(
output_dir: str,
batch_size: int,
dtype: str,
fs: int,
ngpu: int,
seed: int,
num_workers: int,
log_level: Union[int, str],
data_path_and_name_and_type: Sequence[Tuple[str, str, str]],
key_file: Optional[str],
enh_train_config: str,
enh_model_file: str,
allow_variable_data_keys: bool,
normalize_output_wav: bool,
):
assert check_argument_types()
if batch_size > 1:
raise NotImplementedError("batch decoding is not implemented")
if ngpu > 1:
raise NotImplementedError("only single GPU decoding is supported")
logging.basicConfig(
level=log_level,
format="%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s",
)
if ngpu >= 1:
device = "cuda"
else:
device = "cpu"
# 1. Set random-seed
set_all_random_seed(seed)
# 2. Build Enh model
enh_model, enh_train_args = EnhancementTask.build_model_from_file(
enh_train_config, enh_model_file, device)
enh_model.eval()
num_spk = enh_model.num_spk
# 3. Build data-iterator
loader = EnhancementTask.build_streaming_iterator(
data_path_and_name_and_type,
dtype=dtype,
batch_size=batch_size,
key_file=key_file,
num_workers=num_workers,
preprocess_fn=EnhancementTask.build_preprocess_fn(
enh_train_args, False),
collate_fn=EnhancementTask.build_collate_fn(enh_train_args),
allow_variable_data_keys=allow_variable_data_keys,
inference=True,
)
writers = []
for i in range(num_spk):
writers.append(
SoundScpWriter(f"{output_dir}/wavs/{i + 1}",
f"{output_dir}/spk{i + 1}.scp"))
for keys, batch in loader:
assert isinstance(batch, dict), type(batch)
assert all(isinstance(s, str) for s in keys), keys
_bs = len(next(iter(batch.values())))
assert len(keys) == _bs, f"{len(keys)} != {_bs}"
with torch.no_grad():
# a. To device
batch = to_device(batch, device)
# b. Forward Enhancement Frontend
waves, _, _ = enh_model.enh_model.forward_rawwav(
batch["speech_mix"], batch["speech_mix_lengths"])
assert len(waves[0]) == batch_size, len(waves[0])
# FIXME(Chenda): will be incorrect when
# batch size is not 1 or multi-channel case
if normalize_output_wav:
waves = [
(w / abs(w).max(dim=1, keepdim=True)[0] * 0.9).T.cpu().numpy()
for w in waves
] # list[(sample,batch)]
else:
waves = [w.T.cpu().numpy() for w in waves]
for (i, w) in enumerate(waves):
writers[i][keys[0]] = fs, w
for writer in writers:
writer.close()