From f591f33111453c674bb80b8a8fa9c0bff29477e1 Mon Sep 17 00:00:00 2001
From: 维石 <shixian.shi@alibaba-inc.com>
Date: 星期一, 03 六月 2024 15:15:52 +0800
Subject: [PATCH] update libtorch infer
---
runtime/python/libtorch/funasr_torch/paraformer_bin.py | 202 +++++++++++++++++++++++++++++++++++++++++++++++--
1 files changed, 191 insertions(+), 11 deletions(-)
diff --git a/runtime/python/libtorch/funasr_torch/paraformer_bin.py b/runtime/python/libtorch/funasr_torch/paraformer_bin.py
index b7fb14b..e9642c7 100644
--- a/runtime/python/libtorch/funasr_torch/paraformer_bin.py
+++ b/runtime/python/libtorch/funasr_torch/paraformer_bin.py
@@ -1,21 +1,20 @@
# -*- encoding: utf-8 -*-
+import json
+import copy
+import torch
import os.path
+import librosa
+import numpy as np
from pathlib import Path
from typing import List, Union, Tuple
-import copy
-import librosa
-import numpy as np
-
-from .utils.utils import CharTokenizer, Hypothesis, TokenIDConverter, get_logger, read_yaml
-from .utils.postprocess_utils import sentence_postprocess
+from .utils.utils import pad_list
from .utils.frontend import WavFrontend
from .utils.timestamp_utils import time_stamp_lfr6_onnx
+from .utils.postprocess_utils import sentence_postprocess
+from .utils.utils import CharTokenizer, Hypothesis, TokenIDConverter, get_logger, read_yaml
logging = get_logger()
-
-import torch
-import json
class Paraformer:
@@ -32,7 +31,6 @@
device_id: Union[str, int] = "-1",
plot_timestamp_to: str = "",
quantize: bool = False,
- intra_op_num_threads: int = 4,
cache_dir: str = None,
**kwargs,
):
@@ -236,4 +234,186 @@
token = self.converter.ids2tokens(token_int)
token = token[: valid_token_num - self.pred_bias]
# texts = sentence_postprocess(token)
- return token
\ No newline at end of file
+ return token
+
+
+class ContextualParaformer(Paraformer):
+ """
+ Author: Speech Lab of DAMO Academy, Alibaba Group
+ Paraformer: Fast and Accurate Parallel Transformer for Non-autoregressive End-to-End Speech Recognition
+ https://arxiv.org/abs/2206.08317
+ """
+
+ def __init__(
+ self,
+ model_dir: Union[str, Path] = None,
+ batch_size: int = 1,
+ device_id: Union[str, int] = "-1",
+ plot_timestamp_to: str = "",
+ quantize: bool = False,
+ 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
+ )
+
+ if quantize:
+ model_bb_file = os.path.join(model_dir, "model_bb_quant.torchscripts")
+ model_eb_file = os.path.join(model_dir, "model_eb_quant.torchscripts")
+ else:
+ model_bb_file = os.path.join(model_dir, "model_bb.torchscripts")
+ model_eb_file = os.path.join(model_dir, "model_eb.torchscripts")
+
+ if not (os.path.exists(model_eb_file) and os.path.exists(model_bb_file)):
+ print(".onnx is 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)
+
+ # revert token_list into vocab dict
+ self.vocab = {}
+ for i, token in enumerate(token_list):
+ self.vocab[token] = i
+
+ self.converter = TokenIDConverter(token_list)
+ self.tokenizer = CharTokenizer()
+ self.frontend = WavFrontend(cmvn_file=cmvn_file, **config["frontend_conf"])
+
+ self.ort_infer_bb = torch.jit.load(model_bb_file)
+ self.ort_infer_eb = torch.jit.load(model_eb_file)
+ self.device_id = device_id
+
+ self.batch_size = batch_size
+ self.plot_timestamp_to = plot_timestamp_to
+ if "predictor_bias" in config["model_conf"].keys():
+ self.pred_bias = config["model_conf"]["predictor_bias"]
+ else:
+ self.pred_bias = 0
+
+ def __call__(
+ self, wav_content: Union[str, np.ndarray, List[str]], hotwords: str, **kwargs
+ ) -> List:
+ # make hotword list
+ hotwords, hotwords_length = self.proc_hotword(hotwords)
+ # import pdb; pdb.set_trace()
+ [bias_embed] = self.eb_infer(hotwords, hotwords_length)
+ # index from bias_embed
+ bias_embed = bias_embed.transpose(1, 0, 2)
+ _ind = np.arange(0, len(hotwords)).tolist()
+ bias_embed = bias_embed[_ind, hotwords_length.tolist()]
+ 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])
+ bias_embed = np.expand_dims(bias_embed, axis=0)
+ bias_embed = np.repeat(bias_embed, feats.shape[0], axis=0)
+ try:
+ with torch.no_grad():
+ if int(self.device_id) == -1:
+ outputs = self.ort_infer(feats, feats_len)
+ am_scores, valid_token_lens = outputs[0], outputs[1]
+ else:
+ outputs = self.ort_infer(feats.cuda(), feats_len.cuda())
+ am_scores, valid_token_lens = outputs[0].cpu(), outputs[1].cpu()
+ except:
+ # logging.warning(traceback.format_exc())
+ logging.warning("input wav is silence or noise")
+ preds = [""]
+ else:
+ preds = self.decode(am_scores, valid_token_lens)
+ for pred in preds:
+ pred = sentence_postprocess(pred)
+ asr_res.append({"preds": pred})
+ return asr_res
+
+ def proc_hotword(self, hotwords):
+ hotwords = hotwords.split(" ")
+ hotwords_length = [len(i) - 1 for i in hotwords]
+ hotwords_length.append(0)
+ hotwords_length = np.array(hotwords_length)
+
+ # hotwords.append('<s>')
+ def word_map(word):
+ hotwords = []
+ for c in word:
+ if c not in self.vocab.keys():
+ hotwords.append(8403)
+ logging.warning(
+ "oov character {} found in hotword {}, replaced by <unk>".format(c, word)
+ )
+ else:
+ hotwords.append(self.vocab[c])
+ return np.array(hotwords)
+
+ hotword_int = [word_map(i) for i in hotwords]
+ hotword_int.append(np.array([1]))
+ hotwords = pad_list(hotword_int, pad_value=0, max_len=10)
+ return hotwords, hotwords_length
+
+ def bb_infer(
+ self, feats: np.ndarray, feats_len: np.ndarray, bias_embed
+ ) -> Tuple[np.ndarray, np.ndarray]:
+ outputs = self.ort_infer_bb([feats, feats_len, bias_embed])
+ return outputs
+
+ def eb_infer(self, hotwords, hotwords_length):
+ outputs = self.ort_infer_eb([hotwords.astype(np.int32), hotwords_length.astype(np.int32)])
+ return outputs
+
+ def decode(self, am_scores: np.ndarray, token_nums: int) -> List[str]:
+ return [
+ self.decode_one(am_score, token_num)
+ for am_score, token_num in zip(am_scores, token_nums)
+ ]
+
+ def decode_one(self, am_score: np.ndarray, valid_token_num: int) -> List[str]:
+ yseq = am_score.argmax(axis=-1)
+ score = am_score.max(axis=-1)
+ score = np.sum(score, axis=-1)
+
+ # pad with mask tokens to ensure compatibility with sos/eos tokens
+ # asr_model.sos:1 asr_model.eos:2
+ yseq = np.array([1] + yseq.tolist() + [2])
+ hyp = Hypothesis(yseq=yseq, score=score)
+
+ # remove sos/eos and get results
+ last_pos = -1
+ token_int = hyp.yseq[1:last_pos].tolist()
+
+ # remove blank symbol id, which is assumed to be 0
+ token_int = list(filter(lambda x: x not in (0, 2), token_int))
+
+ # Change integer-ids to tokens
+ token = self.converter.ids2tokens(token_int)
+ token = token[: valid_token_num - self.pred_bias]
+ # texts = sentence_postprocess(token)
+ return token
+
+
+class SeacoParaformer(ContextualParaformer):
+ def __init__(self, *args, **kwargs):
+ super().__init__(*args, **kwargs)
+ # no difference with contextual_paraformer in method of calling onnx models
--
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