From aa3fe1a353bde71d106755d030d9e5300fbde328 Mon Sep 17 00:00:00 2001
From: 游雁 <zhifu.gzf@alibaba-inc.com>
Date: 星期一, 22 七月 2024 19:02:15 +0800
Subject: [PATCH] python runtime
---
/dev/null | 38 ------
runtime/python/onnxruntime/funasr_onnx/__init__.py | 2
runtime/python/onnxruntime/setup.py | 3
runtime/python/onnxruntime/funasr_onnx/utils/postprocess_utils.py | 120 ++++++++++++++++++++
runtime/python/onnxruntime/demo_sencevoice_small.py | 19 +++
runtime/python/onnxruntime/funasr_onnx/sensevoice_bin.py | 88 +++++++++-----
runtime/python/onnxruntime/funasr_onnx/utils/sentencepiece_tokenizer.py | 53 ++++++++
7 files changed, 253 insertions(+), 70 deletions(-)
diff --git a/runtime/python/onnxruntime/demo_sencevoice_small.py b/runtime/python/onnxruntime/demo_sencevoice_small.py
new file mode 100644
index 0000000..3b84b64
--- /dev/null
+++ b/runtime/python/onnxruntime/demo_sencevoice_small.py
@@ -0,0 +1,19 @@
+#!/usr/bin/env python3
+# -*- encoding: utf-8 -*-
+# Copyright FunASR (https://github.com/FunAudioLLM/SenseVoice). All Rights Reserved.
+# MIT License (https://opensource.org/licenses/MIT)
+
+from pathlib import Path
+from funasr_onnx import SenseVoiceSmall
+from funasr_onnx.utils.postprocess_utils import rich_transcription_postprocess
+
+
+model_dir = "iic/SenseVoiceSmall"
+
+model = SenseVoiceSmall(model_dir, batch_size=10, quantize=False)
+
+# inference
+wav_or_scp = ["{}/.cache/modelscope/hub/{}/example/en.mp3".format(Path.home(), model_dir)]
+
+res = model(wav_or_scp, language="auto", use_itn=True)
+print([rich_transcription_postprocess(i) for i in res])
diff --git a/runtime/python/onnxruntime/demo_sencevoicesmall.py b/runtime/python/onnxruntime/demo_sencevoicesmall.py
deleted file mode 100644
index 27f0179..0000000
--- a/runtime/python/onnxruntime/demo_sencevoicesmall.py
+++ /dev/null
@@ -1,38 +0,0 @@
-#!/usr/bin/env python3
-# -*- encoding: utf-8 -*-
-# Copyright FunASR (https://github.com/FunAudioLLM/SenseVoice). All Rights Reserved.
-# MIT License (https://opensource.org/licenses/MIT)
-
-import os
-import torch
-from pathlib import Path
-from funasr import AutoModel
-from funasr_onnx import SenseVoiceSmallONNX as SenseVoiceSmall
-from funasr.utils.postprocess_utils import rich_transcription_postprocess
-
-
-model_dir = "iic/SenseVoiceSmall"
-model = AutoModel(
- model=model_dir,
- device="cuda:0",
-)
-
-res = model.export(type="onnx", quantize=False)
-
-# export model init
-model_path = "{}/.cache/modelscope/hub/{}".format(Path.home(), model_dir)
-model_bin = SenseVoiceSmall(model_path)
-
-# build tokenizer
-try:
- from funasr.tokenizer.sentencepiece_tokenizer import SentencepiecesTokenizer
- tokenizer = SentencepiecesTokenizer(bpemodel=os.path.join(model_path, "chn_jpn_yue_eng_ko_spectok.bpe.model"))
-except:
- tokenizer = None
-
-# inference
-wav_or_scp = "/Users/shixian/Downloads/asr_example_hotword.wav"
-language_list = [0]
-textnorm_list = [15]
-res = model_bin(wav_or_scp, language_list, textnorm_list, tokenizer=tokenizer)
-print([rich_transcription_postprocess(i) for i in res])
diff --git a/runtime/python/onnxruntime/funasr_onnx/__init__.py b/runtime/python/onnxruntime/funasr_onnx/__init__.py
index 4256629..cffbcdf 100644
--- a/runtime/python/onnxruntime/funasr_onnx/__init__.py
+++ b/runtime/python/onnxruntime/funasr_onnx/__init__.py
@@ -4,4 +4,4 @@
from .vad_bin import Fsmn_vad_online
from .punc_bin import CT_Transformer
from .punc_bin import CT_Transformer_VadRealtime
-from .sensevoice_bin import SenseVoiceSmallONNX
+from .sensevoice_bin import SenseVoiceSmall
diff --git a/runtime/python/onnxruntime/funasr_onnx/sensevoice_bin.py b/runtime/python/onnxruntime/funasr_onnx/sensevoice_bin.py
index fcfcede..7d6f341 100644
--- a/runtime/python/onnxruntime/funasr_onnx/sensevoice_bin.py
+++ b/runtime/python/onnxruntime/funasr_onnx/sensevoice_bin.py
@@ -20,12 +20,13 @@
get_logger,
read_yaml,
)
+from .utils.sentencepiece_tokenizer import SentencepiecesTokenizer
from .utils.frontend import WavFrontend
logging = get_logger()
-class SenseVoiceSmallONNX:
+class SenseVoiceSmall:
"""
Author: Speech Lab of DAMO Academy, Alibaba Group
Paraformer: Fast and Accurate Parallel Transformer for Non-autoregressive End-to-End Speech Recognition
@@ -43,45 +44,72 @@
cache_dir: str = None,
**kwargs,
):
+
+ if not Path(model_dir).exists():
+ try:
+ from modelscope.hub.snapshot_download import snapshot_download
+ except:
+ raise "You are exporting model from modelscope, please install modelscope and try it again. To install modelscope, you could:\n" "\npip3 install -U modelscope\n" "For the users in China, you could install with the command:\n" "\npip3 install -U modelscope -i https://mirror.sjtu.edu.cn/pypi/web/simple"
+ try:
+ model_dir = snapshot_download(model_dir, cache_dir=cache_dir)
+ except:
+ raise "model_dir must be model_name in modelscope or local path downloaded from modelscope, but is {}".format(
+ model_dir
+ )
+
+ model_file = os.path.join(model_dir, "model.onnx")
if quantize:
model_file = os.path.join(model_dir, "model_quant.onnx")
- else:
- model_file = os.path.join(model_dir, "model.onnx")
+ if not os.path.exists(model_file):
+ print(".onnx does not exist, begin to export onnx")
+ try:
+ from funasr import AutoModel
+ except:
+ raise "You are exporting onnx, please install funasr and try it again. To install funasr, you could:\n" "\npip3 install -U funasr\n" "For the users in China, you could install with the command:\n" "\npip3 install -U funasr -i https://mirror.sjtu.edu.cn/pypi/web/simple"
+
+ model = AutoModel(model=model_dir)
+ model_dir = model.export(type="onnx", quantize=quantize, **kwargs)
config_file = os.path.join(model_dir, "config.yaml")
cmvn_file = os.path.join(model_dir, "am.mvn")
config = read_yaml(config_file)
- # token_list = os.path.join(model_dir, "tokens.json")
- # with open(token_list, "r", encoding="utf-8") as f:
- # token_list = json.load(f)
- # self.converter = TokenIDConverter(token_list)
- self.tokenizer = CharTokenizer()
- config["frontend_conf"]['cmvn_file'] = cmvn_file
+ self.tokenizer = SentencepiecesTokenizer(
+ bpemodel=os.path.join(model_dir, "chn_jpn_yue_eng_ko_spectok.bpe.model")
+ )
+ config["frontend_conf"]["cmvn_file"] = cmvn_file
self.frontend = WavFrontend(**config["frontend_conf"])
self.ort_infer = OrtInferSession(
model_file, device_id, intra_op_num_threads=intra_op_num_threads
)
self.batch_size = batch_size
self.blank_id = 0
+ self.lid_dict = {"auto": 0, "zh": 3, "en": 4, "yue": 7, "ja": 11, "ko": 12, "nospeech": 13}
+ self.lid_int_dict = {24884: 3, 24885: 4, 24888: 7, 24892: 11, 24896: 12, 24992: 13}
+ self.textnorm_dict = {"withitn": 14, "woitn": 15}
+ self.textnorm_int_dict = {25016: 14, 25017: 15}
- def __call__(self,
- wav_content: Union[str, np.ndarray, List[str]],
- language: List,
- textnorm: List,
- tokenizer=None,
- **kwargs) -> List:
+ def __call__(self, wav_content: Union[str, np.ndarray, List[str]], **kwargs):
+
+ language = self.lid_dict[kwargs.get("language", "auto")]
+ use_itn = kwargs.get("use_itn", False)
+ textnorm = kwargs.get("text_norm", None)
+ if textnorm is None:
+ textnorm = "withitn" if use_itn else "woitn"
+ textnorm = self.textnorm_dict[textnorm]
+
waveform_list = self.load_data(wav_content, self.frontend.opts.frame_opts.samp_freq)
waveform_nums = len(waveform_list)
asr_res = []
for beg_idx in range(0, waveform_nums, self.batch_size):
end_idx = min(waveform_nums, beg_idx + self.batch_size)
feats, feats_len = self.extract_feat(waveform_list[beg_idx:end_idx])
- ctc_logits, encoder_out_lens = self.infer(feats,
- feats_len,
- np.array(language, dtype=np.int32),
- np.array(textnorm, dtype=np.int32)
- )
+ ctc_logits, encoder_out_lens = self.infer(
+ feats,
+ feats_len,
+ np.array(language, dtype=np.int32),
+ np.array(textnorm, dtype=np.int32),
+ )
# back to torch.Tensor
ctc_logits = torch.from_numpy(ctc_logits).float()
# support batch_size=1 only currently
@@ -91,11 +119,9 @@
mask = yseq != self.blank_id
token_int = yseq[mask].tolist()
-
- if tokenizer is not None:
- asr_res.append(tokenizer.tokens2text(token_int))
- else:
- asr_res.append(token_int)
+
+ asr_res.append(self.tokenizer.encode(token_int))
+
return asr_res
def load_data(self, wav_content: Union[str, np.ndarray, List[str]], fs: int = None) -> List:
@@ -136,10 +162,12 @@
feats = np.array(feat_res).astype(np.float32)
return feats
- def infer(self,
- feats: np.ndarray,
- feats_len: np.ndarray,
- language: np.ndarray,
- textnorm: np.ndarray,) -> Tuple[np.ndarray, np.ndarray]:
+ def infer(
+ self,
+ feats: np.ndarray,
+ feats_len: np.ndarray,
+ language: np.ndarray,
+ textnorm: np.ndarray,
+ ) -> Tuple[np.ndarray, np.ndarray]:
outputs = self.ort_infer([feats, feats_len, language, textnorm])
return outputs
diff --git a/runtime/python/onnxruntime/funasr_onnx/utils/postprocess_utils.py b/runtime/python/onnxruntime/funasr_onnx/utils/postprocess_utils.py
index da8065e..d144d31 100644
--- a/runtime/python/onnxruntime/funasr_onnx/utils/postprocess_utils.py
+++ b/runtime/python/onnxruntime/funasr_onnx/utils/postprocess_utils.py
@@ -296,3 +296,123 @@
real_word_lists.append(ch)
sentence = "".join(word_lists)
return sentence, real_word_lists
+
+
+emo_dict = {
+ "<|HAPPY|>": "馃槉",
+ "<|SAD|>": "馃様",
+ "<|ANGRY|>": "馃槨",
+ "<|NEUTRAL|>": "",
+ "<|FEARFUL|>": "馃槹",
+ "<|DISGUSTED|>": "馃あ",
+ "<|SURPRISED|>": "馃槷",
+}
+
+event_dict = {
+ "<|BGM|>": "馃幖",
+ "<|Speech|>": "",
+ "<|Applause|>": "馃憦",
+ "<|Laughter|>": "馃榾",
+ "<|Cry|>": "馃槶",
+ "<|Sneeze|>": "馃ぇ",
+ "<|Breath|>": "",
+ "<|Cough|>": "馃ぇ",
+}
+
+lang_dict = {
+ "<|zh|>": "<|lang|>",
+ "<|en|>": "<|lang|>",
+ "<|yue|>": "<|lang|>",
+ "<|ja|>": "<|lang|>",
+ "<|ko|>": "<|lang|>",
+ "<|nospeech|>": "<|lang|>",
+}
+
+emoji_dict = {
+ "<|nospeech|><|Event_UNK|>": "鉂�",
+ "<|zh|>": "",
+ "<|en|>": "",
+ "<|yue|>": "",
+ "<|ja|>": "",
+ "<|ko|>": "",
+ "<|nospeech|>": "",
+ "<|HAPPY|>": "馃槉",
+ "<|SAD|>": "馃様",
+ "<|ANGRY|>": "馃槨",
+ "<|NEUTRAL|>": "",
+ "<|BGM|>": "馃幖",
+ "<|Speech|>": "",
+ "<|Applause|>": "馃憦",
+ "<|Laughter|>": "馃榾",
+ "<|FEARFUL|>": "馃槹",
+ "<|DISGUSTED|>": "馃あ",
+ "<|SURPRISED|>": "馃槷",
+ "<|Cry|>": "馃槶",
+ "<|EMO_UNKNOWN|>": "",
+ "<|Sneeze|>": "馃ぇ",
+ "<|Breath|>": "",
+ "<|Cough|>": "馃樂",
+ "<|Sing|>": "",
+ "<|Speech_Noise|>": "",
+ "<|withitn|>": "",
+ "<|woitn|>": "",
+ "<|GBG|>": "",
+ "<|Event_UNK|>": "",
+}
+
+emo_set = {"馃槉", "馃様", "馃槨", "馃槹", "馃あ", "馃槷"}
+event_set = {
+ "馃幖",
+ "馃憦",
+ "馃榾",
+ "馃槶",
+ "馃ぇ",
+ "馃樂",
+}
+
+
+def format_str_v2(s):
+ sptk_dict = {}
+ for sptk in emoji_dict:
+ sptk_dict[sptk] = s.count(sptk)
+ s = s.replace(sptk, "")
+ emo = "<|NEUTRAL|>"
+ for e in emo_dict:
+ if sptk_dict[e] > sptk_dict[emo]:
+ emo = e
+ for e in event_dict:
+ if sptk_dict[e] > 0:
+ s = event_dict[e] + s
+ s = s + emo_dict[emo]
+
+ for emoji in emo_set.union(event_set):
+ s = s.replace(" " + emoji, emoji)
+ s = s.replace(emoji + " ", emoji)
+ return s.strip()
+
+
+def rich_transcription_postprocess(s):
+ def get_emo(s):
+ return s[-1] if s[-1] in emo_set else None
+
+ def get_event(s):
+ return s[0] if s[0] in event_set else None
+
+ s = s.replace("<|nospeech|><|Event_UNK|>", "鉂�")
+ for lang in lang_dict:
+ s = s.replace(lang, "<|lang|>")
+ s_list = [format_str_v2(s_i).strip(" ") for s_i in s.split("<|lang|>")]
+ new_s = " " + s_list[0]
+ cur_ent_event = get_event(new_s)
+ for i in range(1, len(s_list)):
+ if len(s_list[i]) == 0:
+ continue
+ if get_event(s_list[i]) == cur_ent_event and get_event(s_list[i]) != None:
+ s_list[i] = s_list[i][1:]
+ # else:
+ cur_ent_event = get_event(s_list[i])
+ if get_emo(s_list[i]) != None and get_emo(s_list[i]) == get_emo(new_s):
+ new_s = new_s[:-1]
+ new_s += s_list[i].strip().lstrip()
+ new_s = new_s.replace("The.", " ")
+ return new_s.strip()
diff --git a/runtime/python/onnxruntime/funasr_onnx/utils/sentencepiece_tokenizer.py b/runtime/python/onnxruntime/funasr_onnx/utils/sentencepiece_tokenizer.py
new file mode 100644
index 0000000..966e359
--- /dev/null
+++ b/runtime/python/onnxruntime/funasr_onnx/utils/sentencepiece_tokenizer.py
@@ -0,0 +1,53 @@
+from pathlib import Path
+from typing import Iterable
+from typing import List
+from typing import Union
+
+import sentencepiece as spm
+
+
+class SentencepiecesTokenizer:
+ def __init__(self, bpemodel: Union[Path, str], **kwargs):
+ super().__init__(**kwargs)
+ self.bpemodel = str(bpemodel)
+ # NOTE(kamo):
+ # Don't build SentencePieceProcessor in __init__()
+ # because it's not picklable and it may cause following error,
+ # "TypeError: can't pickle SwigPyObject objects",
+ # when giving it as argument of "multiprocessing.Process()".
+ self.sp = None
+ self._build_sentence_piece_processor()
+
+ def __repr__(self):
+ return f'{self.__class__.__name__}(model="{self.bpemodel}")'
+
+ def _build_sentence_piece_processor(self):
+ # Build SentencePieceProcessor lazily.
+ if self.sp is None:
+ self.sp = spm.SentencePieceProcessor()
+ self.sp.load(self.bpemodel)
+
+ def text2tokens(self, line: str) -> List[str]:
+ self._build_sentence_piece_processor()
+ return self.sp.EncodeAsPieces(line)
+
+ def tokens2text(self, tokens: Iterable[str]) -> str:
+ self._build_sentence_piece_processor()
+ return self.sp.DecodePieces(list(tokens))
+
+ def encode(self, line: str, **kwargs) -> List[int]:
+ self._build_sentence_piece_processor()
+ return self.sp.EncodeAsIds(line)
+
+ def decode(self, line: List[int], **kwargs):
+ self._build_sentence_piece_processor()
+ return self.sp.DecodeIds(line)
+
+ def get_vocab_size(self):
+ return self.sp.GetPieceSize()
+
+ def ids2tokens(self, *args, **kwargs):
+ return self.decode(*args, **kwargs)
+
+ def tokens2ids(self, *args, **kwargs):
+ return self.encode(*args, **kwargs)
diff --git a/runtime/python/onnxruntime/setup.py b/runtime/python/onnxruntime/setup.py
index fa313fe..58a210b 100644
--- a/runtime/python/onnxruntime/setup.py
+++ b/runtime/python/onnxruntime/setup.py
@@ -31,10 +31,11 @@
"librosa",
"onnxruntime>=1.7.0",
"scipy",
- "numpy>=1.19.3",
+ "numpy<=1.26.4",
"kaldi-native-fbank",
"PyYAML>=5.1.2",
"onnx",
+ "sentencepiece",
],
packages=[MODULE_NAME, f"{MODULE_NAME}.utils"],
keywords=["funasr,asr"],
--
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