support wav_file input

haoneng.lhn

2023-04-14 5589b4a6177a0ca6836c304a152befe87f0ddd96

 funasr/models/predictor/cif.py

@@ -234,6 +234,7 @@
        last_fire_place = len_time - 1

        last_fire_remainds = 0.0

        pre_alphas_length = 0

        last_fire = False

 

        mask_chunk_peak_predictor = None

        if cache is not None:

@@ -251,10 +252,15 @@
            if cif_peak[0][len_time - 1 - i] > self.threshold or cif_peak[0][len_time - 1 - i] == self.threshold:

                last_fire_place = len_time - 1 - i

                last_fire_remainds = cif_peak[0][len_time - 1 - i] - self.threshold

                last_fire = True

                break

        last_fire_remainds = torch.tensor([last_fire_remainds], dtype=alphas.dtype).to(alphas.device)

        cache["cif_hidden"] = hidden[:, last_fire_place:, :]

        cache["cif_alphas"] = torch.cat((last_fire_remainds.unsqueeze(0), alphas[:, last_fire_place+1:]), -1)

        if last_fire:

           last_fire_remainds = torch.tensor([last_fire_remainds], dtype=alphas.dtype).to(alphas.device)

           cache["cif_hidden"] = hidden[:, last_fire_place:, :]

           cache["cif_alphas"] = torch.cat((last_fire_remainds.unsqueeze(0), alphas[:, last_fire_place+1:]), -1)

        else:

           cache["cif_hidden"] = hidden

           cache["cif_alphas"] = alphas

        token_num_int = token_num.floor().type(torch.int32).item()

        return acoustic_embeds[:, 0:token_num_int, :], token_num, alphas, cif_peak