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update model class

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维石 2024-06-03 15:47:00 +08:00
parent fd0992af3d
commit d2e9bf0142
2 changed files with 11 additions and 12 deletions
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2
runtime/python/libtorch/demo_contextual_paraformer.py
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@ -7,7 +7,7 @@ device_id = 0 if torch.cuda.is_available() else -1
model = ContextualParaformer(model_dir, batch_size=1, device_id=device_id) # gpu model = ContextualParaformer(model_dir, batch_size=1, device_id=device_id) # gpu
wav_path = "{}/.cache/modelscope/hub/{}/example/asr_example.wav".format(Path.home(), model_dir) wav_path = "{}/.cache/modelscope/hub/{}/example/asr_example.wav".format(Path.home(), model_dir)
hotwords = "你的热词 魔搭 达摩苑" hotwords = "你的热词 魔哒"
result = model(wav_path, hotwords) result = model(wav_path, hotwords)
print(result) print(result)

21
runtime/python/libtorch/funasr_torch/paraformer_bin.py
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@ -282,7 +282,7 @@ class ContextualParaformer(Paraformer):
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" 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 = AutoModel(model=model_dir)
model_dir = model.export(type="onnx", quantize=quantize, **kwargs) model_dir = model.export(type="torchscripts", quantize=quantize, **kwargs)
config_file = os.path.join(model_dir, "config.yaml") config_file = os.path.join(model_dir, "config.yaml")
cmvn_file = os.path.join(model_dir, "am.mvn") cmvn_file = os.path.join(model_dir, "am.mvn")
@ -316,9 +316,9 @@ class ContextualParaformer(Paraformer):
) -> List: ) -> List:
# make hotword list # make hotword list
hotwords, hotwords_length = self.proc_hotword(hotwords) hotwords, hotwords_length = self.proc_hotword(hotwords)
[bias_embed] = self.eb_infer(torch.Tensor(hotwords), torch.Tensor(hotwords_length)) bias_embed = self.eb_infer(torch.Tensor(hotwords))
# index from bias_embed # index from bias_embed
bias_embed = bias_embed.transpose(1, 0, 2) bias_embed = torch.transpose(bias_embed, 0, 1)
_ind = np.arange(0, len(hotwords)).tolist() _ind = np.arange(0, len(hotwords)).tolist()
bias_embed = bias_embed[_ind, hotwords_length.tolist()] bias_embed = bias_embed[_ind, hotwords_length.tolist()]
waveform_list = self.load_data(wav_content, self.frontend.opts.frame_opts.samp_freq) waveform_list = self.load_data(wav_content, self.frontend.opts.frame_opts.samp_freq)
@ -327,15 +327,14 @@ class ContextualParaformer(Paraformer):
for beg_idx in range(0, waveform_nums, self.batch_size): for beg_idx in range(0, waveform_nums, self.batch_size):
end_idx = min(waveform_nums, beg_idx + 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]) feats, feats_len = self.extract_feat(waveform_list[beg_idx:end_idx])
bias_embed = np.expand_dims(bias_embed, axis=0) bias_embed = torch.unsqueeze(bias_embed, 0).repeat(feats.shape[0], 1, 1)
bias_embed = np.repeat(bias_embed, feats.shape[0], axis=0)
try: try:
with torch.no_grad(): with torch.no_grad():
if int(self.device_id) == -1: if int(self.device_id) == -1:
outputs = self.bb_infer(feats, feats_len) outputs = self.bb_infer(feats, feats_len, bias_embed)
am_scores, valid_token_lens = outputs[0], outputs[1] am_scores, valid_token_lens = outputs[0], outputs[1]
else: else:
outputs = self.bb_infer_infer(feats.cuda(), feats_len.cuda()) outputs = self.bb_infer_infer(feats.cuda(), feats_len.cuda(), bias_embed)
am_scores, valid_token_lens = outputs[0].cpu(), outputs[1].cpu() am_scores, valid_token_lens = outputs[0].cpu(), outputs[1].cpu()
except: except:
# logging.warning(traceback.format_exc()) # logging.warning(traceback.format_exc())
@ -374,12 +373,12 @@ class ContextualParaformer(Paraformer):
def bb_infer( def bb_infer(
self, feats, feats_len, bias_embed self, feats, feats_len, bias_embed
) -> Tuple[np.ndarray, np.ndarray]: ):
outputs = self.ort_infer_bb([feats, feats_len, bias_embed]) outputs = self.ort_infer_bb(feats, feats_len, bias_embed)
return outputs return outputs
def eb_infer(self, hotwords, hotwords_length): def eb_infer(self, hotwords):
outputs = self.ort_infer_eb([hotwords, hotwords_length]) outputs = self.ort_infer_eb(hotwords.long())
return outputs return outputs
def decode(self, am_scores: np.ndarray, token_nums: int) -> List[str]: def decode(self, am_scores: np.ndarray, token_nums: int) -> List[str]: