def forced_align(
self,
name: str,
target_corpus_key: str,
flow: Union[str, List[str], Tuple[str],
rasr.FlagDependentFlowAttribute],
feature_scorer: Union[str, List[str], Tuple[str], rasr.FeatureScorer],
feature_scorer_corpus_key: str = None,
dump_alignment: bool = False,
**kwargs,
):
"""
TODO: docstring
:param name:
:param target_corpus_key:
:param flow:
:param feature_scorer:
:param feature_scorer_corpus_key:
:param dump_alignment:
:param kwargs:
:return:
"""
selected_feature_scorer = meta.select_element(
self.feature_scorers, feature_scorer_corpus_key, feature_scorer)
self.align(
name=name,
corpus=target_corpus_key,
flow=flow,
feature_scorer=selected_feature_scorer,
kwargs=kwargs,
)
align_job = self.jobs[target_corpus_key]["alignment_%s" % name]
align_job.add_alias("forced_alignment/alignment_%s" % name)
tk.register_output(
"forced_alignment/alignment_%s.bundle" % name,
align_job.out_alignment_bundle,
)
if dump_alignment:
dump_job = mm.DumpAlignmentJob(
crp=self.crp[target_corpus_key],
feature_flow=meta.select_element(self.feature_flows,
target_corpus_key, flow),
original_alignment=meta.select_element(self.alignments,
target_corpus_key,
name),
)
self.jobs[target_corpus_key]["alignment_dump_%s" % name] = dump_job
dump_job.add_alias("forced_alignment/alignment_dump_%s" % name)
tk.register_output(
"forced_alignment/alignment_dump_%s.bundle" % name,
dump_job.out_alignment_bundle,
)
def vtln_warping_mixtures(
self,
name: str,
corpus_key: str,
feature_flow_key: str,
feature_scorer: Union[str, List[str], Tuple[str], rasr.FeatureScorer],
alignment: Union[str, List[str], Tuple[str],
rasr.FlagDependentFlowAttribute],
splits: int,
accs_per_split: int,
):
"""
TODO: docstring
:param name:
:param corpus_key:
:param feature_flow_key:
:param feature_scorer:
:param alignment:
:param splits:
:param accs_per_split:
:return:
"""
feature_flow_key = self.feature_flows[corpus_key][feature_flow_key]
warp = vtln.ScoreFeaturesWithWarpingFactorsJob(
crp=self.crp[corpus_key],
feature_flow=feature_flow_key,
feature_scorer=meta.select_element(self.feature_scorers,
corpus_key, feature_scorer),
alignment=meta.select_element(self.alignments, corpus_key,
alignment),
)
warp.rqmt = {"time": 24, "cpu": 1, "mem": 2}
self.jobs[corpus_key]["vtln_warping_map_%s" % name] = warp
seq = meta.TrainWarpingFactorsSequence(
self.crp[corpus_key],
None,
feature_flow_key,
warp.warping_map,
warp.alphas_file,
["accumulate"] +
meta.split_and_accumulate_sequence(splits, accs_per_split),
)
self.mixtures[corpus_key]["vtln_warping_mix_%s" %
name] = seq.selected_mixtures
self.vtln_files[corpus_key][name + "_alphas_file"] = warp.alphas_file
self.vtln_files[corpus_key][name + "_warping_map"] = warp.warping_map
self.vtln_files[corpus_key][name + "_mixtures"] = seq.selected_mixtures
def single_density_mixtures(self, name: str, corpus_key: str,
feature_flow_key: str, alignment: str):
"""
TODO: docstring
:param name:
:param corpus_key:
:param feature_flow_key:
:param alignment:
:return:
"""
self.estimate_mixtures(
name=name,
corpus=corpus_key,
flow=feature_flow_key,
alignment=meta.select_element(self.alignments, corpus_key,
(corpus_key, alignment, -1)),
split_first=False,
)
def sat_recognition(
self,
prev_ctm: str,
feature_cache: Union[str, List[str], Tuple[str],
rasr.FlagDependentFlowAttribute],
cache_regex: str,
cmllr_mixtures: Union[str, List[str], Tuple[str],
rasr.FlagDependentFlowAttribute],
train_corpus_key: str,
name: str,
iters: List[int],
lm_scales: Union[float, List[float]],
feature_scorer_key: Tuple[str, str],
optimize_am_lm_scale: bool,
corpus_key: str,
feature_flow: str,
pronunciation_scales: Union[float, List[float]],
search_parameters: dict,
rtf: float,
mem: float,
parallelize_conversion: bool,
lattice_to_ctm_kwargs: dict,
**kwargs,
):
"""
TODO: docstring
:param prev_ctm:
:param feature_cache:
:param cache_regex:
:param cmllr_mixtures:
:param train_corpus_key:
:param name:
:param iters:
:param lm_scales:
:param feature_scorer_key:
:param optimize_am_lm_scale:
:param corpus_key:
:param feature_flow:
:param pronunciation_scales:
:param search_parameters:
:param rtf:
:param mem:
:param parallelize_conversion:
:param lattice_to_ctm_kwargs:
:param kwargs:
:return:
"""
prev_ctm_key = f"recog_{train_corpus_key}-{prev_ctm[0]}-{corpus_key}-ps{prev_ctm[1]:02.2f}-lm{prev_ctm[2]:02.2f}-iter{prev_ctm[3]:02d}{prev_ctm[4]}"
assert prev_ctm_key in self.ctm_files[corpus_key], (
"the previous recognition stage '%s' did not provide the required recognition: %s"
% (prev_ctm, prev_ctm_key))
recognized_corpus = corpus_recipes.ReplaceTranscriptionFromCtmJob(
self.corpora[corpus_key].corpus_file,
self.ctm_files[corpus_key][prev_ctm_key],
)
speaker_seq = corpus_recipes.SegmentCorpusBySpeakerJob(
self.corpora[corpus_key].corpus_file)
overlay_key = f"{corpus_key}_{name}_ps{prev_ctm[1]:02.2f}-lm{prev_ctm[2]:02.2f}-iter{prev_ctm[3]:02d}{prev_ctm[4]}_sat"
self.add_overlay(corpus_key, overlay_key)
self.crp[overlay_key].corpus_config = copy.deepcopy(
self.crp[corpus_key].corpus_config)
self.crp[
overlay_key].corpus_config.file = recognized_corpus.output_corpus_path
self.crp[overlay_key].segment_path = copy.deepcopy(
self.crp[corpus_key].segment_path)
self.corpora[overlay_key] = copy.deepcopy(self.corpora[corpus_key])
self.corpora[
overlay_key].corpus_file = recognized_corpus.output_corpus_path
alignment = mm.AlignmentJob(
crp=self.crp[overlay_key],
feature_flow=self.feature_flows[overlay_key][feature_flow],
feature_scorer=self.default_mixture_scorer(
meta.select_element(self.mixtures, corpus_key,
(train_corpus_key, cmllr_mixtures)), ),
)
self.estimate_cmllr(
name=name,
corpus_key=overlay_key,
feature_cache=meta.select_element(self.feature_caches, corpus_key,
feature_cache),
feature_flow_key=feature_flow,
cache_regex=cache_regex,
alignment=alignment.out_alignment_path,
mixtures=meta.select_element(self.mixtures, corpus_key,
(train_corpus_key, cmllr_mixtures)),
overlay_key=overlay_key,
)
self.feature_flows[corpus_key][
"%s+cmllr" % feature_flow] = sat.add_cmllr_transform(
feature_net=self.feature_flows[corpus_key][feature_flow],
map_file=speaker_seq.out_cluster_map_file,
transform_dir=self.jobs[overlay_key]["cmllr"].transforms,
)
with tk.block(f"{name}_recognition"):
self.recognition(
name=name,
iters=iters,
lm_scales=lm_scales,
feature_scorer_key=feature_scorer_key,
optimize_am_lm_scale=optimize_am_lm_scale,
corpus_key=corpus_key,
feature_flow=feature_flow + "+cmllr",
pronunciation_scales=pronunciation_scales,
search_parameters=search_parameters,
rtf=rtf,
mem=mem,
parallelize_conversion=parallelize_conversion,
lattice_to_ctm_kwargs=lattice_to_ctm_kwargs,
**kwargs,
)
def sat_training(
self,
name: str,
corpus_key: str,
feature_cache: Union[str, List[str], Tuple[str],
rasr.FlagDependentFlowAttribute],
feature_flow_key: str,
cache_regex: str,
alignment: Union[str, List[str], Tuple[str],
rasr.FlagDependentFlowAttribute],
mixtures: Union[str, List[str], Tuple[str],
rasr.FlagDependentFlowAttribute],
splits: int,
accs_per_split: int,
align_keep_values: Optional[dict] = None,
**kwargs,
):
"""
TODO: docstring
:param name:
:param corpus_key:
:param feature_cache:
:param feature_flow_key:
:param cache_regex:
:param alignment:
:param mixtures:
:param splits:
:param accs_per_split:
:param align_keep_values:
:param kwargs:
:return:
"""
self.estimate_cmllr(
name=name,
corpus_key=corpus_key,
feature_cache=meta.select_element(self.feature_caches, corpus_key,
feature_cache),
feature_flow_key=feature_flow_key,
cache_regex=cache_regex,
alignment=meta.select_element(self.alignments, corpus_key,
alignment),
mixtures=meta.select_element(self.mixtures, corpus_key, mixtures),
)
action_sequence = (["accumulate"] +
meta.align_then_split_and_accumulate_sequence(
splits, accs_per_split, mark_align=False) +
["align!"])
akv = dict(**self.default_align_keep_values)
if align_keep_values is not None:
akv.update(align_keep_values)
self.train(
name=name,
corpus=corpus_key,
sequence=action_sequence,
flow="%s+cmllr" % feature_flow_key,
initial_alignment=meta.select_element(self.alignments, corpus_key,
alignment),
align_keep_values=akv,
**kwargs,
)
self.jobs[corpus_key]["train_{}".format(
name)].selected_alignment_jobs[-1].add_alias(
"train/{}_{}_align_last".format(corpus_key, name))
self.jobs[corpus_key]["train_{}".format(
name)].selected_mixture_jobs[-1].add_alias(
"train/{}_{}_mix_last".format(corpus_key, name))
tk.register_output(
"train/{}_{}_align_bundle_last".format(corpus_key, name),
self.jobs[corpus_key]["train_{}".format(
name)].selected_alignment_jobs[-1].out_alignment_bundle,
)
tk.register_output(
"train/{}_{}_mix_last".format(corpus_key, name),
self.jobs[corpus_key]["train_{}".format(
name)].selected_mixture_jobs[-1].out_mixtures,
)
def cart_and_lda(
self,
name: str,
corpus_key: str,
initial_flow_key: str,
context_flow_key: str,
context_size: int,
alignment: Union[str, List[str], Tuple[str],
rasr.FlagDependentFlowAttribute],
num_dim: int,
num_iter: int,
eigenvalue_args: dict,
generalized_eigenvalue_args: dict,
**kwargs,
):
"""
TODO: docstring
:param name:
:param corpus_key:
:param initial_flow_key:
:param context_flow_key:
:param context_size:
:param alignment:
:param num_dim:
:param num_iter:
:param eigenvalue_args:
:param generalized_eigenvalue_args:
:param kwargs:
:return:
"""
for f in self.feature_flows.values():
f["{}+context".format(context_flow_key)] = lda.add_context_flow(
feature_net=f[context_flow_key],
max_size=context_size,
right=int(context_size / 2.0),
)
cart_lda = meta.CartAndLDA(
original_crp=self.crp[corpus_key],
initial_flow=self.feature_flows[corpus_key][initial_flow_key],
context_flow=self.feature_flows[corpus_key]["{}+context".format(
context_flow_key)],
alignment=meta.select_element(self.alignments, corpus_key,
alignment),
questions=self.cart_questions,
num_dim=num_dim,
num_iter=num_iter,
eigenvalue_args=eigenvalue_args,
generalized_eigenvalue_args=generalized_eigenvalue_args,
)
self.jobs[corpus_key]["cart_and_lda_{}_{}".format(corpus_key,
name)] = cart_lda
self.lda_matrices[corpus_key][name] = cart_lda.last_lda_matrix
self.cart_trees[corpus_key][name] = cart_lda.last_cart_tree
tk.register_output(
"{}_{}_last_num_cart_labels".format(corpus_key, name),
cart_lda.last_num_cart_labels,
)
tk.register_output("{}_{}.tree.xml.gz".format(corpus_key, name),
cart_lda.last_cart_tree)
for f in self.feature_flows.values():
f["{}+context+lda".format(
context_flow_key)] = features.add_linear_transform(
f["{}+context".format(context_flow_key)],
cart_lda.last_lda_matrix)
for crp in self.crp.values():
crp.acoustic_model_config.state_tying.type = "cart"
crp.acoustic_model_config.state_tying.file = cart_lda.last_cart_tree
state_tying_job = allophones.DumpStateTyingJob(self.crp[corpus_key])
tk.register_output(
"{}_{}_state_tying".format(corpus_key, name),
state_tying_job.out_state_tying,
)
def monophone_training(
self,
name: str,
corpus_key: str,
linear_alignment_args: dict,
feature_energy_flow_key: str,
feature_flow: Union[str, List[str], Tuple[str],
rasr.FlagDependentFlowAttribute],
align_iter: int,
splits: int,
accs_per_split: int,
align_keep_values: Optional[dict] = None,
**kwargs,
):
"""
TODO: docstring
:param name:
:param corpus_key:
:param linear_alignment_args:
:param feature_energy_flow_key:
:param feature_flow:
:param align_iter:
:param splits:
:param accs_per_split:
:param align_keep_values:
:param kwargs:
:return:
"""
if linear_alignment_args is not None:
self.linear_alignment(
name,
corpus_key,
feature_energy_flow_key,
prefix=f"{corpus_key}_",
**linear_alignment_args,
)
action_sequence = meta.align_and_accumulate_sequence(
align_iter, 1, mark_accumulate=False, mark_align=False)
action_sequence += meta.split_and_accumulate_sequence(
splits, accs_per_split) + ["align!"]
akv = dict(**self.default_align_keep_values)
if align_keep_values is not None:
akv.update(align_keep_values)
self.train(
name=name,
corpus=corpus_key,
sequence=action_sequence,
flow=feature_flow,
initial_mixtures=meta.select_element(
self.mixtures, corpus_key, "linear_alignment_{}".format(name)),
align_keep_values=akv,
**kwargs,
)
self.jobs[corpus_key]["train_{}".format(
name)].selected_alignment_jobs[-1].add_alias(
"train/{}_{}_align_last".format(corpus_key, name))
self.jobs[corpus_key]["train_{}".format(
name)].selected_mixture_jobs[-1].add_alias(
"train/{}_{}_mix_last".format(corpus_key, name))
tk.register_output(
"train/{}_{}_align_bundle_last".format(corpus_key, name),
self.jobs[corpus_key]["train_{}".format(
name)].selected_alignment_jobs[-1].out_alignment_bundle,
)
tk.register_output(
"train/{}_{}_mix_last".format(corpus_key, name),
self.jobs[corpus_key]["train_{}".format(
name)].selected_mixture_jobs[-1].out_mixtures,
)
state_tying_job = allophones.DumpStateTyingJob(self.crp[corpus_key])
tk.register_output(
"{}_{}_state_tying".format(corpus_key, name),
state_tying_job.out_state_tying,
)
