update cif onnx

shixian.shi

2023-02-27 508e518b12f52eb84843e9cad4b3e51165bb52fb

update cif onnx

 .gitignore

@@ -7,4 +7,6 @@
init_model/
*.tar.gz
test_local/
RapidASR
RapidASR
export/*
*.pyc

 funasr/export/models/predictor/cif.py

@@ -48,11 +48,11 @@
        alphas = torch.nn.functional.relu(alphas * self.smooth_factor - self.noise_threshold)

        mask = mask.transpose(-1, -2).float()

        alphas = alphas * mask

		
        alphas = alphas.squeeze(-1)

		
        token_num = alphas.sum(-1)

        

        mask = mask.squeeze(-1)

        hidden, alphas, token_num = self.tail_process_fn(hidden, alphas, mask=mask)

        acoustic_embeds, cif_peak = cif(hidden, alphas, self.threshold)

        

        return acoustic_embeds, token_num, alphas, cif_peak

@@ -63,12 +63,14 @@
        

        zeros_t = torch.zeros((b, 1), dtype=torch.float32, device=alphas.device)

        ones_t = torch.ones_like(zeros_t)



        mask_1 = torch.cat([mask, zeros_t], dim=1)

        mask_2 = torch.cat([ones_t, mask], dim=1)

        mask = mask_2 - mask_1

        tail_threshold = mask * tail_threshold

        alphas = torch.cat([alphas, tail_threshold], dim=1)

		
        alphas = torch.cat([alphas, zeros_t], dim=1)

        alphas = torch.add(alphas, tail_threshold)



        zeros = torch.zeros((b, 1, d), dtype=hidden.dtype).to(hidden.device)

        hidden = torch.cat([hidden, zeros], dim=1)

        token_num = alphas.sum(dim=-1)


 funasr/runtime/python/onnxruntime/demo.py

@@ -1,10 +1,10 @@

from rapid_paraformer import Paraformer

model_dir = "/nfs/zhifu.gzf/export/damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch"
model_dir = "/Users/shixian/code/funasr2/export/damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch"
model = Paraformer(model_dir, batch_size=1)

wav_path = ['/nfs/zhifu.gzf/export/damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch/example/asr_example.wav']
wav_path = ['/Users/shixian/code/funasr2/export/damo/speech_paraformer-tiny-commandword_asr_nat-zh-cn-16k-vocab544-pytorch/example/asr_example.wav']

result = model(wav_path)
print(result)