From a75bbb028e5966ddf02aae5bea05909be9a99826 Mon Sep 17 00:00:00 2001
From: 游雁 <zhifu.gzf@alibaba-inc.com>
Date: 星期四, 11 一月 2024 17:36:30 +0800
Subject: [PATCH] funasr1.0 paraformer_streaming
---
/dev/null | 14 ----
funasr/models/paraformer_streaming/model.py | 82 +++++++++++++++------------
funasr/models/scama/sanm_encoder.py | 2
funasr/models/paraformer/cif_predictor.py | 11 ++-
funasr/utils/load_utils.py | 2
examples/industrial_data_pretraining/paraformer_streaming/demo.py | 50 +++++++++-------
examples/industrial_data_pretraining/paraformer_streaming/infer.sh | 2
7 files changed, 85 insertions(+), 78 deletions(-)
diff --git a/examples/industrial_data_pretraining/paraformer_streaming/demo.py b/examples/industrial_data_pretraining/paraformer_streaming/demo.py
index 0036e77..9923a04 100644
--- a/examples/industrial_data_pretraining/paraformer_streaming/demo.py
+++ b/examples/industrial_data_pretraining/paraformer_streaming/demo.py
@@ -3,36 +3,44 @@
# Copyright FunASR (https://github.com/alibaba-damo-academy/FunASR). All Rights Reserved.
# MIT License (https://opensource.org/licenses/MIT)
-# from funasr import AutoModel
-#
-# model = AutoModel(model="damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch", model_revison="v2.0.0")
-#
-# res = model(input="https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_zh.wav")
-# print(res)
+from funasr import AutoModel
+chunk_size = [0, 10, 5] #[0, 10, 5] 600ms, [0, 8, 4] 480ms
+encoder_chunk_look_back = 4 #number of chunks to lookback for encoder self-attention
+decoder_chunk_look_back = 1 #number of encoder chunks to lookback for decoder cross-attention
-from funasr import AutoFrontend
-
-frontend = AutoFrontend(model="/Users/zhifu/Downloads/modelscope_models/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-online", model_revison="v2.0.0")
-
+model = AutoModel(model="damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-online", model_revison="v2.0.0")
+cache = {}
+res = model(input="https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_zh.wav",
+ cache=cache,
+ is_final=True,
+ chunk_size=chunk_size,
+ encoder_chunk_look_back=encoder_chunk_look_back,
+ decoder_chunk_look_back=decoder_chunk_look_back,
+ )
+print(res)
import soundfile
-speech, sample_rate = soundfile.read("/Users/zhifu/Downloads/modelscope_models/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-online/example/asr_example.wav")
+import os
-chunk_size = [0, 10, 5] #[0, 10, 5] 600ms, [0, 8, 4] 480ms
+speech, sample_rate = soundfile.read(os.path.expanduser('~')+
+ "/.cache/modelscope/hub/damo/"+
+ "speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-online/"+
+ "example/asr_example.wav")
+
chunk_stride = chunk_size[1] * 960 # 600ms銆�480ms
-# first chunk, 600ms
cache = {}
for i in range(int(len((speech)-1)/chunk_stride+1)):
speech_chunk = speech[i*chunk_stride:(i+1)*chunk_stride]
- fbanks = frontend(input=speech_chunk,
- batch_size=2,
- cache=cache)
-
-
-# for batch_idx, fbank_dict in enumerate(fbanks):
-# res = model(**fbank_dict)
-# print(res)
\ No newline at end of file
+ is_final = i == int(len((speech)-1)/chunk_stride+1)
+ res = model(input=speech_chunk,
+ cache=cache,
+ is_final=is_final,
+ chunk_size=chunk_size,
+ encoder_chunk_look_back=encoder_chunk_look_back,
+ decoder_chunk_look_back=decoder_chunk_look_back,
+ )
+ print(res)
diff --git a/examples/industrial_data_pretraining/paraformer_streaming/finetune.sh b/examples/industrial_data_pretraining/paraformer_streaming/finetune.sh
deleted file mode 100644
index 6dca09f..0000000
--- a/examples/industrial_data_pretraining/paraformer_streaming/finetune.sh
+++ /dev/null
@@ -1,14 +0,0 @@
-
-# download model
-local_path_root=../modelscope_models
-mkdir -p ${local_path_root}
-local_path=${local_path_root}/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch
-git clone https://www.modelscope.cn/damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch.git ${local_path}
-
-
-python funasr/bin/train.py \
-+model="../modelscope_models/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch" \
-+token_list="../modelscope_models/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch/tokens.txt" \
-+train_data_set_list="data/list/audio_datasets.jsonl" \
-+output_dir="outputs/debug/ckpt/funasr2/exp2" \
-+device="cpu"
\ No newline at end of file
diff --git a/examples/industrial_data_pretraining/paraformer_streaming/infer.sh b/examples/industrial_data_pretraining/paraformer_streaming/infer.sh
index 9436628..77e839b 100644
--- a/examples/industrial_data_pretraining/paraformer_streaming/infer.sh
+++ b/examples/industrial_data_pretraining/paraformer_streaming/infer.sh
@@ -1,5 +1,5 @@
-model="damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch"
+model="damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-online"
model_revision="v2.0.0"
python funasr/bin/inference.py \
diff --git a/funasr/models/paraformer/cif_predictor.py b/funasr/models/paraformer/cif_predictor.py
index 383d9ca..b06fa43 100644
--- a/funasr/models/paraformer/cif_predictor.py
+++ b/funasr/models/paraformer/cif_predictor.py
@@ -205,7 +205,8 @@
return acoustic_embeds, token_num, alphas, cif_peak
- def forward_chunk(self, hidden, cache=None):
+ def forward_chunk(self, hidden, cache=None, **kwargs):
+ is_final = kwargs.get("is_final", False)
batch_size, len_time, hidden_size = hidden.shape
h = hidden
context = h.transpose(1, 2)
@@ -226,14 +227,14 @@
if cache is not None and "chunk_size" in cache:
alphas[:, :cache["chunk_size"][0]] = 0.0
- if "is_final" in cache and not cache["is_final"]:
+ if not is_final:
alphas[:, sum(cache["chunk_size"][:2]):] = 0.0
if cache is not None and "cif_alphas" in cache and "cif_hidden" in cache:
cache["cif_hidden"] = to_device(cache["cif_hidden"], device=hidden.device)
cache["cif_alphas"] = to_device(cache["cif_alphas"], device=alphas.device)
hidden = torch.cat((cache["cif_hidden"], hidden), dim=1)
alphas = torch.cat((cache["cif_alphas"], alphas), dim=1)
- if cache is not None and "is_final" in cache and cache["is_final"]:
+ if cache is not None and is_final:
tail_hidden = torch.zeros((batch_size, 1, hidden_size), device=hidden.device)
tail_alphas = torch.tensor([[self.tail_threshold]], device=alphas.device)
tail_alphas = torch.tile(tail_alphas, (batch_size, 1))
@@ -277,7 +278,7 @@
max_token_len = max(token_length)
if max_token_len == 0:
- return hidden, torch.stack(token_length, 0)
+ return hidden, torch.stack(token_length, 0), None, None
list_ls = []
for b in range(batch_size):
pad_frames = torch.zeros((max_token_len - token_length[b], hidden_size), device=alphas.device)
@@ -291,7 +292,7 @@
cache["cif_alphas"] = torch.unsqueeze(cache["cif_alphas"], axis=0)
cache["cif_hidden"] = torch.stack(cache_hiddens, axis=0)
cache["cif_hidden"] = torch.unsqueeze(cache["cif_hidden"], axis=0)
- return torch.stack(list_ls, 0), torch.stack(token_length, 0)
+ return torch.stack(list_ls, 0), torch.stack(token_length, 0), None, None
def tail_process_fn(self, hidden, alphas, token_num=None, mask=None):
diff --git a/funasr/models/paraformer_streaming/model.py b/funasr/models/paraformer_streaming/model.py
index 304c0f7..927b091 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,
):
@@ -526,9 +516,10 @@
self.init_beam_search(**kwargs)
self.nbest = kwargs.get("nbest", 1)
- cache = kwargs.get("cache", {})
+
if len(cache) == 0:
self.init_cache(cache, **kwargs)
+ _is_final = kwargs.get("is_final", False)
meta_data = {}
chunk_size = kwargs.get("chunk_size", [0, 10, 5])
@@ -542,22 +533,41 @@
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 = len(audio_sample) // chunk_stride_samples + int(_is_final)
+ m = 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
diff --git a/funasr/models/scama/sanm_encoder.py b/funasr/models/scama/sanm_encoder.py
index 4bf6ef0..5e28db7 100644
--- a/funasr/models/scama/sanm_encoder.py
+++ b/funasr/models/scama/sanm_encoder.py
@@ -423,7 +423,9 @@
xs_pad: torch.Tensor,
ilens: torch.Tensor,
cache: dict = None,
+ **kwargs,
):
+ is_final = kwargs.get("is_final", False)
xs_pad *= self.output_size() ** 0.5
if self.embed is None:
xs_pad = xs_pad
diff --git a/funasr/utils/load_utils.py b/funasr/utils/load_utils.py
index 39b708a..bb9cf01 100644
--- a/funasr/utils/load_utils.py
+++ b/funasr/utils/load_utils.py
@@ -43,7 +43,7 @@
elif isinstance(data_or_path_or_list, str) and data_type == "text" and tokenizer is not None:
data_or_path_or_list = tokenizer.encode(data_or_path_or_list)
elif isinstance(data_or_path_or_list, np.ndarray): # audio sample point
- data_or_path_or_list = np.squeeze(data_or_path_or_list) # [n_samples,]
+ data_or_path_or_list = torch.from_numpy(data_or_path_or_list).squeeze() # [n_samples,]
else:
pass
# print(f"unsupport data type: {data_or_path_or_list}, return raw data")
--
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