From c3442d9566f5a2011c95b0d2998958a1b5348564 Mon Sep 17 00:00:00 2001
From: shixian.shi <shixian.shi@alibaba-inc.com>
Date: 星期五, 12 一月 2024 18:04:42 +0800
Subject: [PATCH] update device
---
funasr/models/paraformer_streaming/model.py | 97 ++++++++++++++++++++++++++++--------------------
1 files changed, 57 insertions(+), 40 deletions(-)
diff --git a/funasr/models/paraformer_streaming/model.py b/funasr/models/paraformer_streaming/model.py
index 304c0f7..b736aa9 100644
--- a/funasr/models/paraformer_streaming/model.py
+++ b/funasr/models/paraformer_streaming/model.py
@@ -64,8 +64,8 @@
super().__init__(*args, **kwargs)
- import pdb;
- pdb.set_trace()
+ # import pdb;
+ # pdb.set_trace()
self.sampling_ratio = kwargs.get("sampling_ratio", 0.2)
@@ -375,11 +375,10 @@
return pre_acoustic_embeds, pre_token_length, pre_alphas, pre_peak_index
- def calc_predictor_chunk(self, encoder_out, encoder_out_lens, cache=None):
-
- pre_acoustic_embeds, pre_token_length = \
- self.predictor.forward_chunk(encoder_out, cache["encoder"])
- return pre_acoustic_embeds, pre_token_length
+ def calc_predictor_chunk(self, encoder_out, encoder_out_lens, cache=None, **kwargs):
+ is_final = kwargs.get("is_final", False)
+
+ return self.predictor.forward_chunk(encoder_out, cache["encoder"], is_final=is_final)
def cal_decoder_with_predictor(self, encoder_out, encoder_out_lens, sematic_embeds, ys_pad_lens):
decoder_outs = self.decoder(
@@ -416,7 +415,7 @@
"chunk_size": chunk_size}
cache["decoder"] = cache_decoder
cache["frontend"] = {}
- cache["prev_samples"] = []
+ cache["prev_samples"] = torch.empty(0)
return cache
@@ -432,12 +431,12 @@
speech.to(device=kwargs["device"]), speech_lengths.to(device=kwargs["device"])
# Encoder
- encoder_out, encoder_out_lens = self.encode_chunk(speech, speech_lengths, cache=cache)
+ encoder_out, encoder_out_lens = self.encode_chunk(speech, speech_lengths, cache=cache, is_final=kwargs.get("is_final", False))
if isinstance(encoder_out, tuple):
encoder_out = encoder_out[0]
# predictor
- predictor_outs = self.calc_predictor_chunk(encoder_out, encoder_out_lens, cache=cache)
+ predictor_outs = self.calc_predictor_chunk(encoder_out, encoder_out_lens, cache=cache, is_final=kwargs.get("is_final", False))
pre_acoustic_embeds, pre_token_length, alphas, pre_peak_index = predictor_outs[0], predictor_outs[1], \
predictor_outs[2], predictor_outs[3]
pre_token_length = pre_token_length.round().long()
@@ -476,10 +475,7 @@
)
nbest_hyps = [Hypothesis(yseq=yseq, score=score)]
for nbest_idx, hyp in enumerate(nbest_hyps):
- ibest_writer = None
- if ibest_writer is None and kwargs.get("output_dir") is not None:
- writer = DatadirWriter(kwargs.get("output_dir"))
- ibest_writer = writer[f"{nbest_idx + 1}best_recog"]
+
# remove sos/eos and get results
last_pos = -1
if isinstance(hyp.yseq, list):
@@ -490,22 +486,15 @@
# 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))
- if tokenizer is not None:
- # Change integer-ids to tokens
- token = tokenizer.ids2tokens(token_int)
- text = tokenizer.tokens2text(token)
-
- text_postprocessed, _ = postprocess_utils.sentence_postprocess(token)
-
- result_i = {"key": key[i], "text": text_postprocessed}
-
- if ibest_writer is not None:
- ibest_writer["token"][key[i]] = " ".join(token)
- # ibest_writer["text"][key[i]] = text
- ibest_writer["text"][key[i]] = text_postprocessed
- else:
- result_i = {"key": key[i], "token_int": token_int}
- results.append(result_i)
+
+ # Change integer-ids to tokens
+ token = tokenizer.ids2tokens(token_int)
+ # text = tokenizer.tokens2text(token)
+
+ result_i = token
+
+
+ results.extend(result_i)
return results
@@ -515,6 +504,7 @@
key: list = None,
tokenizer=None,
frontend=None,
+ cache: dict={},
**kwargs,
):
@@ -526,38 +516,65 @@
self.init_beam_search(**kwargs)
self.nbest = kwargs.get("nbest", 1)
- cache = kwargs.get("cache", {})
+
if len(cache) == 0:
self.init_cache(cache, **kwargs)
+
meta_data = {}
chunk_size = kwargs.get("chunk_size", [0, 10, 5])
- chunk_stride_samples = chunk_size[1] * 960 # 600ms
+ chunk_stride_samples = int(chunk_size[1] * 960) # 600ms
time1 = time.perf_counter()
- audio_sample_list = load_audio_text_image_video(data_in, fs=frontend.fs, audio_fs=kwargs.get("fs", 16000),
- data_type=kwargs.get("data_type", "sound"),
- tokenizer=tokenizer)
+ cfg = {"is_final": kwargs.get("is_final", False)}
+ audio_sample_list = load_audio_text_image_video(data_in,
+ fs=frontend.fs,
+ audio_fs=kwargs.get("fs", 16000),
+ data_type=kwargs.get("data_type", "sound"),
+ tokenizer=tokenizer,
+ cache=cfg,
+ )
+ _is_final = cfg["is_final"] # if data_in is a file or url, set is_final=True
+
time2 = time.perf_counter()
meta_data["load_data"] = f"{time2 - time1:0.3f}"
assert len(audio_sample_list) == 1, "batch_size must be set 1"
- audio_sample = cache["prev_samples"] + audio_sample_list[0]
+ audio_sample = torch.cat((cache["prev_samples"], audio_sample_list[0]))
- n = len(audio_sample) // chunk_stride_samples
- m = len(audio_sample) % chunk_stride_samples
+ n = int(len(audio_sample) // chunk_stride_samples + int(_is_final))
+ m = int(len(audio_sample) % chunk_stride_samples * (1-int(_is_final)))
+ tokens = []
for i in range(n):
+ kwargs["is_final"] = _is_final and i == n -1
audio_sample_i = audio_sample[i*chunk_stride_samples:(i+1)*chunk_stride_samples]
# extract fbank feats
speech, speech_lengths = extract_fbank([audio_sample_i], data_type=kwargs.get("data_type", "sound"),
- frontend=frontend, cache=cache["frontend"])
+ frontend=frontend, cache=cache["frontend"], is_final=kwargs["is_final"])
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
- result_i = self.generate_chunk(speech, speech_lengths, **kwargs)
+ tokens_i = self.generate_chunk(speech, speech_lengths, key=key, tokenizer=tokenizer, cache=cache, frontend=frontend, **kwargs)
+ tokens.extend(tokens_i)
+
+ text_postprocessed, _ = postprocess_utils.sentence_postprocess(tokens)
+
+ result_i = {"key": key[0], "text": text_postprocessed}
+ result = [result_i]
+
cache["prev_samples"] = audio_sample[:-m]
+ if _is_final:
+ self.init_cache(cache, **kwargs)
+
+ if kwargs.get("output_dir"):
+ writer = DatadirWriter(kwargs.get("output_dir"))
+ ibest_writer = writer[f"{1}best_recog"]
+ ibest_writer["token"][key[0]] = " ".join(tokens)
+ ibest_writer["text"][key[0]] = text_postprocessed
+
+ return result, meta_data
--
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