From f41a1276ff18cb9fd3d837dcda84a7650637d579 Mon Sep 17 00:00:00 2001
From: haoneng.lhn <haoneng.lhn@alibaba-inc.com>
Date: 星期四, 14 九月 2023 12:25:44 +0800
Subject: [PATCH] add paraformer online opt infer code
---
funasr/bin/asr_inference_launch.py | 78 ++++++++++++++++++++++++++++++++------
1 files changed, 65 insertions(+), 13 deletions(-)
diff --git a/funasr/bin/asr_inference_launch.py b/funasr/bin/asr_inference_launch.py
index ffb0b26..1b38f8f 100644
--- a/funasr/bin/asr_inference_launch.py
+++ b/funasr/bin/asr_inference_launch.py
@@ -236,6 +236,7 @@
timestamp_infer_config: Union[Path, str] = None,
timestamp_model_file: Union[Path, str] = None,
param_dict: dict = None,
+ decoding_ind: int = 0,
**kwargs,
):
ncpu = kwargs.get("ncpu", 1)
@@ -290,6 +291,7 @@
nbest=nbest,
hotword_list_or_file=hotword_list_or_file,
clas_scale=clas_scale,
+ decoding_ind=decoding_ind,
)
speech2text = Speech2TextParaformer(**speech2text_kwargs)
@@ -312,6 +314,7 @@
**kwargs,
):
+ decoding_ind = None
hotword_list_or_file = None
if param_dict is not None:
hotword_list_or_file = param_dict.get('hotword')
@@ -319,6 +322,8 @@
hotword_list_or_file = kwargs['hotword']
if hotword_list_or_file is not None or 'hotword' in kwargs:
speech2text.hotword_list = speech2text.generate_hotwords_list(hotword_list_or_file)
+ if param_dict is not None and "decoding_ind" in param_dict:
+ decoding_ind = param_dict["decoding_ind"]
# 3. Build data-iterator
if data_path_and_name_and_type is None and raw_inputs is not None:
@@ -365,6 +370,7 @@
# N-best list of (text, token, token_int, hyp_object)
time_beg = time.time()
+ batch["decoding_ind"] = decoding_ind
results = speech2text(**batch)
if len(results) < 1:
hyp = Hypothesis(score=0.0, scores={}, states={}, yseq=[])
@@ -421,7 +427,7 @@
else:
text_postprocessed, word_lists = postprocessed_result[0], postprocessed_result[1]
item = {'key': key, 'value': text_postprocessed}
- if timestamp_postprocessed != "" or len(timestamp) == 0:
+ if timestamp_postprocessed != "":
item['timestamp'] = timestamp_postprocessed
asr_result_list.append(item)
finish_count += 1
@@ -711,7 +717,7 @@
item = {'key': key, 'value': text_postprocessed_punc}
if text_postprocessed != "":
item['text_postprocessed'] = text_postprocessed
- if time_stamp_postprocessed != "" or len(time_stamp) == 0:
+ if time_stamp_postprocessed != "":
item['time_stamp'] = time_stamp_postprocessed
item['sentences'] = time_stamp_sentence(punc_id_list, time_stamp_postprocessed, text_postprocessed)
@@ -834,37 +840,72 @@
data = yaml.load(f, Loader=yaml.Loader)
return data
- def _prepare_cache(cache: dict = {}, chunk_size=[5, 10, 5], batch_size=1):
+ def _prepare_cache(cache: dict = {}, chunk_size=[5, 10, 5], encoder_chunk_look_back=0,
+ decoder_chunk_look_back=0, batch_size=1):
if len(cache) > 0:
return cache
config = _read_yaml(asr_train_config)
enc_output_size = config["encoder_conf"]["output_size"]
feats_dims = config["frontend_conf"]["n_mels"] * config["frontend_conf"]["lfr_m"]
cache_en = {"start_idx": 0, "cif_hidden": torch.zeros((batch_size, 1, enc_output_size)),
- "cif_alphas": torch.zeros((batch_size, 1)), "chunk_size": chunk_size, "last_chunk": False,
+ "cif_alphas": torch.zeros((batch_size, 1)), "chunk_size": chunk_size,
+ "encoder_chunk_look_back": encoder_chunk_look_back, "last_chunk": False, "opt": None,
"feats": torch.zeros((batch_size, chunk_size[0] + chunk_size[2], feats_dims)), "tail_chunk": False}
cache["encoder"] = cache_en
- cache_de = {"decode_fsmn": None}
+ cache_de = {"decode_fsmn": None, "decoder_chunk_look_back": decoder_chunk_look_back, "opt": None}
cache["decoder"] = cache_de
return cache
- def _cache_reset(cache: dict = {}, chunk_size=[5, 10, 5], batch_size=1):
+ def _cache_reset(cache: dict = {}, chunk_size=[5, 10, 5], encoder_chunk_look_back=0,
+ decoder_chunk_look_back=0, batch_size=1):
if len(cache) > 0:
config = _read_yaml(asr_train_config)
enc_output_size = config["encoder_conf"]["output_size"]
feats_dims = config["frontend_conf"]["n_mels"] * config["frontend_conf"]["lfr_m"]
cache_en = {"start_idx": 0, "cif_hidden": torch.zeros((batch_size, 1, enc_output_size)),
- "cif_alphas": torch.zeros((batch_size, 1)), "chunk_size": chunk_size, "last_chunk": False,
- "feats": torch.zeros((batch_size, chunk_size[0] + chunk_size[2], feats_dims)),
- "tail_chunk": False}
+ "cif_alphas": torch.zeros((batch_size, 1)), "chunk_size": chunk_size,
+ "encoder_chunk_look_back": encoder_chunk_look_back, "last_chunk": False, "opt": None,
+ "feats": torch.zeros((batch_size, chunk_size[0] + chunk_size[2], feats_dims)), "tail_chunk": False}
cache["encoder"] = cache_en
- cache_de = {"decode_fsmn": None}
+ cache_de = {"decode_fsmn": None, "decoder_chunk_look_back": decoder_chunk_look_back, "opt": None}
cache["decoder"] = cache_de
return cache
+
+ #def _prepare_cache(cache: dict = {}, chunk_size=[5, 10, 5], batch_size=1):
+ # if len(cache) > 0:
+ # return cache
+ # config = _read_yaml(asr_train_config)
+ # enc_output_size = config["encoder_conf"]["output_size"]
+ # feats_dims = config["frontend_conf"]["n_mels"] * config["frontend_conf"]["lfr_m"]
+ # cache_en = {"start_idx": 0, "cif_hidden": torch.zeros((batch_size, 1, enc_output_size)),
+ # "cif_alphas": torch.zeros((batch_size, 1)), "chunk_size": chunk_size, "last_chunk": False,
+ # "feats": torch.zeros((batch_size, chunk_size[0] + chunk_size[2], feats_dims)), "tail_chunk": False}
+ # cache["encoder"] = cache_en
+
+ # cache_de = {"decode_fsmn": None}
+ # cache["decoder"] = cache_de
+
+ # return cache
+
+ #def _cache_reset(cache: dict = {}, chunk_size=[5, 10, 5], batch_size=1):
+ # if len(cache) > 0:
+ # config = _read_yaml(asr_train_config)
+ # enc_output_size = config["encoder_conf"]["output_size"]
+ # feats_dims = config["frontend_conf"]["n_mels"] * config["frontend_conf"]["lfr_m"]
+ # cache_en = {"start_idx": 0, "cif_hidden": torch.zeros((batch_size, 1, enc_output_size)),
+ # "cif_alphas": torch.zeros((batch_size, 1)), "chunk_size": chunk_size, "last_chunk": False,
+ # "feats": torch.zeros((batch_size, chunk_size[0] + chunk_size[2], feats_dims)),
+ # "tail_chunk": False}
+ # cache["encoder"] = cache_en
+
+ # cache_de = {"decode_fsmn": None}
+ # cache["decoder"] = cache_de
+
+ # return cache
def _forward(
data_path_and_name_and_type,
@@ -893,24 +934,34 @@
is_final = False
cache = {}
chunk_size = [5, 10, 5]
+ encoder_chunk_look_back = 0
+ decoder_chunk_look_back = 0
if param_dict is not None and "cache" in param_dict:
cache = param_dict["cache"]
if param_dict is not None and "is_final" in param_dict:
is_final = param_dict["is_final"]
if param_dict is not None and "chunk_size" in param_dict:
chunk_size = param_dict["chunk_size"]
+ if param_dict is not None and "encoder_chunk_look_back" in param_dict:
+ encoder_chunk_look_back = param_dict["encoder_chunk_look_back"]
+ if encoder_chunk_look_back > 0:
+ chunk_size[0] = 0
+ if param_dict is not None and "decoder_chunk_look_back" in param_dict:
+ decoder_chunk_look_back = param_dict["decoder_chunk_look_back"]
# 7 .Start for-loop
# FIXME(kamo): The output format should be discussed about
raw_inputs = torch.unsqueeze(raw_inputs, axis=0)
asr_result_list = []
- cache = _prepare_cache(cache, chunk_size=chunk_size, batch_size=1)
+ cache = _prepare_cache(cache, chunk_size=chunk_size, encoder_chunk_look_back=encoder_chunk_look_back,
+ decoder_chunk_look_back=decoder_chunk_look_back, batch_size=1)
item = {}
if data_path_and_name_and_type is not None and data_path_and_name_and_type[2] == "sound":
sample_offset = 0
speech_length = raw_inputs.shape[1]
stride_size = chunk_size[1] * 960
- cache = _prepare_cache(cache, chunk_size=chunk_size, batch_size=1)
+ cache = _prepare_cache(cache, chunk_size=chunk_size, encoder_chunk_look_back=encoder_chunk_look_back,
+ decoder_chunk_look_back=decoder_chunk_look_back, batch_size=1)
final_result = ""
for sample_offset in range(0, speech_length, min(stride_size, speech_length - sample_offset)):
if sample_offset + stride_size >= speech_length - 1:
@@ -931,7 +982,8 @@
asr_result_list.append(item)
if is_final:
- cache = _cache_reset(cache, chunk_size=chunk_size, batch_size=1)
+ cache = _cache_reset(cache, chunk_size=chunk_size, batch_size=1,
+ encoder_chunk_look_back=encoder_chunk_look_back, decoder_chunk_look_back=decoder_chunk_look_back)
return asr_result_list
return _forward
--
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