From 523e902edbc47f3b9feaadd49cd971e8aac627c7 Mon Sep 17 00:00:00 2001
From: 游雁 <zhifu.gzf@alibaba-inc.com>
Date: 星期三, 27 十二月 2023 15:53:06 +0800
Subject: [PATCH] funasr1.0
---
funasr/models/bici_paraformer/model.py | 86 ++++++++++++++++++++++++-------------------
1 files changed, 48 insertions(+), 38 deletions(-)
diff --git a/funasr/models/bici_paraformer/model.py b/funasr/models/bici_paraformer/model.py
index 52eac87..c37ba12 100644
--- a/funasr/models/bici_paraformer/model.py
+++ b/funasr/models/bici_paraformer/model.py
@@ -30,6 +30,7 @@
from funasr.register import tables
from funasr.models.ctc.ctc import CTC
+
from funasr.models.paraformer.model import Paraformer
@tables.register("model_classes", "BiCifParaformer")
@@ -211,10 +212,11 @@
loss, stats, weight = force_gatherable((loss, stats, batch_size), loss.device)
return loss, stats, weight
-
+
+
def generate(self,
- data_in: list,
- data_lengths: list = None,
+ data_in,
+ data_lengths=None,
key: list = None,
tokenizer=None,
frontend=None,
@@ -230,17 +232,23 @@
self.nbest = kwargs.get("nbest", 1)
meta_data = {}
- # extract fbank feats
- time1 = time.perf_counter()
- audio_sample_list = load_audio(data_in, fs=frontend.fs, audio_fs=kwargs.get("fs", 16000))
- time2 = time.perf_counter()
- meta_data["load_data"] = f"{time2 - time1:0.3f}"
- speech, speech_lengths = extract_fbank(audio_sample_list, data_type=kwargs.get("data_type", "sound"),
- frontend=self.frontend)
- time3 = time.perf_counter()
- meta_data["extract_feat"] = f"{time3 - time2:0.3f}"
- meta_data[
- "batch_data_time"] = speech_lengths.sum().item() * frontend.frame_shift * frontend.lfr_n / 1000
+ if isinstance(data_in, torch.Tensor): # fbank
+ speech, speech_lengths = data_in, data_lengths
+ if len(speech.shape) < 3:
+ speech = speech[None, :, :]
+ if speech_lengths is None:
+ speech_lengths = speech.shape[1]
+ else:
+ # extract fbank feats
+ time1 = time.perf_counter()
+ audio_sample_list = load_audio(data_in, fs=frontend.fs, audio_fs=kwargs.get("fs", 16000))
+ time2 = time.perf_counter()
+ meta_data["load_data"] = f"{time2 - time1:0.3f}"
+ speech, speech_lengths = extract_fbank(audio_sample_list, data_type=kwargs.get("data_type", "sound"),
+ frontend=frontend)
+ time3 = time.perf_counter()
+ meta_data["extract_feat"] = f"{time3 - time2:0.3f}"
+ meta_data["batch_data_time"] = speech_lengths.sum().item() * frontend.frame_shift * frontend.lfr_n / 1000
speech.to(device=kwargs["device"]), speech_lengths.to(device=kwargs["device"])
@@ -261,9 +269,8 @@
decoder_out, ys_pad_lens = decoder_outs[0], decoder_outs[1]
# BiCifParaformer, test no bias cif2
-
_, _, us_alphas, us_peaks = self.calc_predictor_timestamp(encoder_out, encoder_out_lens,
- pre_token_length)
+ pre_token_length)
results = []
b, n, d = decoder_out.size()
@@ -302,27 +309,30 @@
# remove blank symbol id, which is assumed to be 0
token_int = list(filter(lambda x: x != self.eos and x != self.sos and x != self.blank_id, token_int))
- # Change integer-ids to tokens
- token = tokenizer.ids2tokens(token_int)
- text = tokenizer.tokens2text(token)
-
- _, timestamp = ts_prediction_lfr6_standard(us_alphas[i][:encoder_out_lens[i] * 3],
- us_peaks[i][:encoder_out_lens[i] * 3],
- copy.copy(token),
- vad_offset=kwargs.get("begin_time", 0))
-
- text_postprocessed, time_stamp_postprocessed, word_lists = postprocess_utils.sentence_postprocess(token, timestamp)
-
- result_i = {"key": key[i], "token": token, "text": text, "text_postprocessed": text_postprocessed,
- "time_stamp_postprocessed": time_stamp_postprocessed,
- "word_lists": word_lists
- }
- results.append(result_i)
-
- if ibest_writer is not None:
- ibest_writer["token"][key[i]] = " ".join(token)
- ibest_writer["text"][key[i]] = text
- ibest_writer["text_postprocessed"][key[i]] = text_postprocessed
+ if tokenizer is not None:
+ # Change integer-ids to tokens
+ token = tokenizer.ids2tokens(token_int)
+ text = tokenizer.tokens2text(token)
+ _, timestamp = ts_prediction_lfr6_standard(us_alphas[i][:encoder_out_lens[i] * 3],
+ us_peaks[i][:encoder_out_lens[i] * 3],
+ copy.copy(token),
+ vad_offset=kwargs.get("begin_time", 0))
+
+ text_postprocessed, time_stamp_postprocessed, word_lists = postprocess_utils.sentence_postprocess(
+ token, timestamp)
+
+ result_i = {"key": key[i], "text": text_postprocessed,
+ "timestamp": time_stamp_postprocessed,
+ }
+
+ if ibest_writer is not None:
+ ibest_writer["token"][key[i]] = " ".join(token)
+ # ibest_writer["text"][key[i]] = text
+ ibest_writer["timestamp"][key[i]] = time_stamp_postprocessed
+ ibest_writer["text"][key[i]] = text_postprocessed
+ else:
+ result_i = {"key": key[i], "token_int": token_int}
+ results.append(result_i)
- return results, meta_data
+ return results, meta_data
\ No newline at end of file
--
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