From 3258d2be0ad4944f0c7a359164cc25f2a32504c9 Mon Sep 17 00:00:00 2001
From: shixian.shi <shixian.shi@alibaba-inc.com>
Date: 星期二, 21 三月 2023 14:01:29 +0800
Subject: [PATCH] debugging
---
funasr/bin/asr_inference_paraformer.py | 29 +++++++++++++++++++++++++----
1 files changed, 25 insertions(+), 4 deletions(-)
diff --git a/funasr/bin/asr_inference_paraformer.py b/funasr/bin/asr_inference_paraformer.py
index 588b1bc..7e159fd 100644
--- a/funasr/bin/asr_inference_paraformer.py
+++ b/funasr/bin/asr_inference_paraformer.py
@@ -43,6 +43,7 @@
from funasr.models.e2e_asr_paraformer import BiCifParaformer, ContextualParaformer
from funasr.export.models.e2e_asr_paraformer import Paraformer as Paraformer_export
from funasr.utils.timestamp_tools import ts_prediction_lfr6_standard
+from funasr.bin.tp_inference import SpeechText2Timestamp
class Speech2Text:
@@ -50,7 +51,7 @@
Examples:
>>> import soundfile
- >>> speech2text = Speech2Text("asr_config.yml", "asr.pth")
+ >>> speech2text = Speech2Text("asr_config.yml", "asr.pb")
>>> audio, rate = soundfile.read("speech.wav")
>>> speech2text(audio)
[(text, token, token_int, hypothesis object), ...]
@@ -245,7 +246,7 @@
decoder_out, ys_pad_lens = decoder_outs[0], decoder_outs[1]
if isinstance(self.asr_model, BiCifParaformer):
- _, _, us_alphas, us_cif_peak = self.asr_model.calc_predictor_timestamp(enc, enc_len,
+ _, _, us_alphas, us_peaks = self.asr_model.calc_predictor_timestamp(enc, enc_len,
pre_token_length) # test no bias cif2
results = []
@@ -292,7 +293,7 @@
if isinstance(self.asr_model, BiCifParaformer):
_, timestamp = ts_prediction_lfr6_standard(us_alphas[i],
- us_cif_peak[i],
+ us_peaks[i],
copy.copy(token),
vad_offset=begin_time)
results.append((text, token, token_int, hyp, timestamp, enc_len_batch_total, lfr_factor))
@@ -540,7 +541,8 @@
ngram_weight: float = 0.9,
nbest: int = 1,
num_workers: int = 1,
-
+ timestamp_infer_config: Union[Path, str] = None,
+ timestamp_model_file: Union[Path, str] = None,
**kwargs,
):
inference_pipeline = inference_modelscope(
@@ -604,6 +606,8 @@
nbest: int = 1,
num_workers: int = 1,
output_dir: Optional[str] = None,
+ timestamp_infer_config: Union[Path, str] = None,
+ timestamp_model_file: Union[Path, str] = None,
param_dict: dict = None,
**kwargs,
):
@@ -660,6 +664,15 @@
speech2text = Speech2TextExport(**speech2text_kwargs)
else:
speech2text = Speech2Text(**speech2text_kwargs)
+
+ if timestamp_model_file is not None:
+ speechtext2timestamp = SpeechText2Timestamp(
+ timestamp_cmvn_file=cmvn_file,
+ timestamp_model_file=timestamp_model_file,
+ timestamp_infer_config=timestamp_infer_config,
+ )
+ else:
+ speechtext2timestamp = None
def _forward(
data_path_and_name_and_type,
@@ -743,8 +756,16 @@
key = keys[batch_id]
for n, result in zip(range(1, nbest + 1), result):
+ # import pdb; pdb.set_trace()
text, token, token_int, hyp = result[0], result[1], result[2], result[3]
time_stamp = None if len(result) < 5 else result[4]
+ # conduct timestamp prediction here
+ if time_stamp is None and speechtext2timestamp:
+ ts_batch = {}
+ ts_batch['speech'] = batch['speech'][batch_id].squeeze(0)
+ ts_batch['speech_lengths'] = torch.tensor([batch['speech_lengths'][batch_id]])
+ ts_batch['text_lengths'] = torch.tensor([len(token)])
+ import pdb; pdb.set_trace()
# Create a directory: outdir/{n}best_recog
if writer is not None:
ibest_writer = writer[f"{n}best_recog"]
--
Gitblit v1.9.1