update

haoneng.lhn

2023-03-29 d0d8684b964f06ab81279fa11a3725aaff01161c

update

 funasr/bin/asr_inference_paraformer_streaming.py

@@ -42,6 +42,7 @@
from funasr.models.frontend.wav_frontend import WavFrontend
from funasr.models.e2e_asr_paraformer import BiCifParaformer, ContextualParaformer
from funasr.export.models.e2e_asr_paraformer import Paraformer as Paraformer_export
np.set_printoptions(threshold=np.inf)

class Speech2Text:
    """Speech2Text class
@@ -203,7 +204,6 @@
        # Input as audio signal
        if isinstance(speech, np.ndarray):
            speech = torch.tensor(speech)

        if self.frontend is not None:
            feats, feats_len = self.frontend.forward(speech, speech_lengths)
            feats = to_device(feats, device=self.device)
@@ -213,13 +213,16 @@
            feats = speech
            feats_len = speech_lengths
        lfr_factor = max(1, (feats.size()[-1] // 80) - 1)
        feats_len = cache["encoder"]["stride"] + cache["encoder"]["pad_left"] + cache["encoder"]["pad_right"]
        feats = feats[:,cache["encoder"]["start_idx"]:cache["encoder"]["start_idx"]+feats_len,:]
        feats_len = torch.tensor([feats_len])
        batch = {"speech": feats, "speech_lengths": feats_len, "cache": cache}

        # a. To device
        batch = to_device(batch, device=self.device)

        # b. Forward Encoder
        enc, enc_len = self.asr_model.encode_chunk(**batch)
        enc, enc_len = self.asr_model.encode_chunk(feats, feats_len, cache)
        if isinstance(enc, tuple):
            enc = enc[0]
        # assert len(enc) == 1, len(enc)
@@ -592,7 +595,6 @@
        if data_path_and_name_and_type is None and raw_inputs is not None:
            if isinstance(raw_inputs, np.ndarray):
                raw_inputs = torch.tensor(raw_inputs)

        is_final = False
        if param_dict is not None and "cache" in param_dict:
            cache = param_dict["cache"]
@@ -605,62 +607,87 @@
        asr_result = ""
        wait = True
        if len(cache) == 0:
            cache["encoder"] = {"start_idx": 0, "pad_left": 0, "stride": 10, "pad_right": 5, "cif_hidden": None, "cif_alphas": None}
            cache["encoder"] = {"start_idx": 0, "pad_left": 0, "stride": 10, "pad_right": 5, "cif_hidden": None, "cif_alphas": None, "is_final": is_final, "left": 0, "right": 0}
            cache_de = {"decode_fsmn": None}
            cache["decoder"] = cache_de
            cache["first_chunk"] = True
            cache["speech"] = []
            cache["chunk_index"] = 0
            cache["speech_chunk"] = []
            cache["accum_speech"] = 0

        if raw_inputs is not None:
            if len(cache["speech"]) == 0:
                cache["speech"] = raw_inputs
            else:
                cache["speech"] = torch.cat([cache["speech"], raw_inputs], dim=0)
            if len(cache["speech_chunk"]) == 0:
                cache["speech_chunk"] = raw_inputs
            else:
                cache["speech_chunk"] = torch.cat([cache["speech_chunk"], raw_inputs], dim=0)
            while len(cache["speech_chunk"]) >= 960:
            cache["accum_speech"] += len(raw_inputs)
            while cache["accum_speech"] >= 960:
                if cache["first_chunk"]:
                    if len(cache["speech_chunk"]) >= 14400:
                        speech = torch.unsqueeze(cache["speech_chunk"][0:14400], axis=0)
                        speech_length = torch.tensor([14400])
                    if cache["accum_speech"] >= 14400:
                        speech = torch.unsqueeze(cache["speech"], axis=0)
                        speech_length = torch.tensor([len(cache["speech"])])
                        cache["encoder"]["pad_left"] = 5 
                        cache["encoder"]["pad_right"] = 5 
                        cache["encoder"]["stride"] = 10
                        cache["encoder"]["left"] = 5
                        cache["encoder"]["right"] = 0
                        results = speech2text(cache, speech, speech_length)
                        cache["speech_chunk"]= cache["speech_chunk"][4800:]
                        cache["accum_speech"] -= 4800
                        cache["first_chunk"] = False
                        cache["encoder"]["start_idx"] = -5
                        cache["encoder"]["is_final"] = False
                        wait = False
                    else:
                        if is_final:
                            cache["encoder"]["stride"] = len(cache["speech_chunk"]) // 960
                            cache["encoder"]["stride"] = len(cache["speech"]) // 960
                            cache["encoder"]["pad_left"] = 0
                            cache["encoder"]["pad_right"] = 0
                            speech = torch.unsqueeze(cache["speech_chunk"], axis=0)
                            speech_length = torch.tensor([len(cache["speech_chunk"])])
                            speech = torch.unsqueeze(cache["speech"], axis=0)
                            speech_length = torch.tensor([len(cache["speech"])])
                            results = speech2text(cache, speech, speech_length)
                            cache["speech_chunk"] = []
                            cache["accum_speech"] = 0
                            wait = False
                        else:
                            break
                else:
                    if len(cache["speech_chunk"]) >= 19200:
                    if cache["accum_speech"] >= 19200:
                        cache["encoder"]["start_idx"] += 10
                        cache["encoder"]["stride"] = 10
                        cache["encoder"]["pad_left"] = 5
                        speech = torch.unsqueeze(cache["speech_chunk"][:19200], axis=0)
                        speech_length = torch.tensor([19200])
                        cache["encoder"]["pad_right"] = 5
                        cache["encoder"]["left"] = 0
                        cache["encoder"]["right"] = 0
                        speech = torch.unsqueeze(cache["speech"], axis=0)
                        speech_length = torch.tensor([len(cache["speech"])])
                        results = speech2text(cache, speech, speech_length)
                        cache["speech_chunk"] = cache["speech_chunk"][9600:]
                        cache["accum_speech"] -= 9600
                        wait = False
                    else:
                        if is_final:
                            cache["encoder"]["stride"] = len(cache["speech_chunk"]) // 960
                            cache["encoder"]["pad_right"] = 0
                            speech = torch.unsqueeze(cache["speech_chunk"], axis=0)
                            speech_length = torch.tensor([len(cache["speech_chunk"])])
                            results = speech2text(cache, speech, speech_length)
                            cache["speech_chunk"] = []
                            wait = False
                            cache["encoder"]["is_final"] = True
                            if cache["accum_speech"] >= 14400:
                                cache["encoder"]["start_idx"] += 10
                                cache["encoder"]["stride"] = 10
                                cache["encoder"]["pad_left"] = 5
                                cache["encoder"]["pad_right"] = 5
                                cache["encoder"]["left"] = 0
                                cache["encoder"]["right"] = cache["accum_speech"] // 960 - 15
                                speech = torch.unsqueeze(cache["speech"], axis=0)
                                speech_length = torch.tensor([len(cache["speech"])])
                                results = speech2text(cache, speech, speech_length)
                                cache["accum_speech"] -= 9600
                                wait = False
                            else:
                                cache["encoder"]["start_idx"] += 10
                                cache["encoder"]["stride"] = cache["accum_speech"] // 960 - 5
                                cache["encoder"]["pad_left"] = 5
                                cache["encoder"]["pad_right"] = 0
                                cache["encoder"]["left"] = 0
                                cache["encoder"]["right"] = 0
                                speech = torch.unsqueeze(cache["speech"], axis=0)
                                speech_length = torch.tensor([len(cache["speech"])])
                                results = speech2text(cache, speech, speech_length)
                                cache["accum_speech"] = 0
                                wait = False
                        else:
                            break
                

 funasr/models/e2e_asr_paraformer.py

@@ -370,19 +370,10 @@
                encoder_out, encoder_out_lens
            )

        assert encoder_out.size(0) == speech.size(0), (
            encoder_out.size(),
            speech.size(0),
        )
        assert encoder_out.size(1) <= encoder_out_lens.max(), (
            encoder_out.size(),
            encoder_out_lens.max(),
        )

        if intermediate_outs is not None:
            return (encoder_out, intermediate_outs), encoder_out_lens

        return encoder_out, encoder_out_lens
        return encoder_out, torch.tensor([encoder_out.size(1)])

    def calc_predictor(self, encoder_out, encoder_out_lens):


 funasr/models/predictor/cif.py

@@ -200,6 +200,7 @@
        return acoustic_embeds, token_num, alphas, cif_peak



    def forward_chunk(self, hidden, cache=None):

        b, t, d = hidden.size()

        h = hidden

        context = h.transpose(1, 2)

        queries = self.pad(context)

@@ -220,10 +221,19 @@
            alphas = alphas * mask_chunk_predictor

      

        if cache is not None:

            if cache["is_final"]:

                alphas[:, cache["stride"] + cache["pad_left"] - 1] += 0.45

            if cache["cif_hidden"] is not None:

                hidden = torch.cat((cache["cif_hidden"], hidden), 1)

            if cache["cif_alphas"] is not None:

                alphas = torch.cat((cache["cif_alphas"], alphas), -1)



        #if cache["is_final"]:

        #    tail_threshold = torch.tensor([self.tail_threshold], dtype=alphas.dtype).to(alphas.device)

        #    tail_threshold = torch.reshape(tail_threshold, (1, 1))

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

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

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



        token_num = alphas.sum(-1)

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

@@ -240,8 +250,9 @@
                pre_alphas_length = cache["cif_alphas"].size(-1)

                mask_chunk_peak_predictor[:, :pre_alphas_length] = 1.0

            mask_chunk_peak_predictor[:, pre_alphas_length + cache["pad_left"]:pre_alphas_length + cache["stride"] + cache["pad_left"]] = 1.0

            #if cache["is_final"]:

            #    mask_chunk_peak_predictor[:, -1] = 1.0

            



        if mask_chunk_peak_predictor is not None:

            cif_peak = cif_peak * mask_chunk_peak_predictor.squeeze(-1)

        


 funasr/modules/embedding.py

@@ -8,7 +8,7 @@

import math
import torch

import torch.nn.functional as F

def _pre_hook(
    state_dict,
@@ -409,9 +409,18 @@

    def forward_chunk(self, x, cache=None):
        start_idx = 0
        pad_left = 0
        pad_right = 0
        batch_size, timesteps, input_dim = x.size()
        if cache is not None:
            start_idx = cache["start_idx"]
            pad_left = cache["left"]
            pad_right = cache["right"]
        positions = torch.arange(1, timesteps+start_idx+1)[None, :]
        position_encoding = self.encode(positions, input_dim, x.dtype).to(x.device)
        return x + position_encoding[:, start_idx: start_idx + timesteps]
        outputs = x + position_encoding[:, start_idx: start_idx + timesteps]
        outputs = outputs.transpose(1,2)
        outputs = F.pad(outputs, (pad_left, pad_right))
        outputs = outputs.transpose(1,2)
        return outputs