add BiCifParaformer

funasr/bin/asr_inference_paraformer_vad_punc.py

@@ -14,6 +14,7 @@ from typing import Dict
 from typing import Any
 from typing import List
 import math
+import copy
 import numpy as np
 import torch
 from typeguard import check_argument_types
@@ -38,7 +39,7 @@ from funasr.utils.types import str_or_none
 from funasr.utils import asr_utils, wav_utils, postprocess_utils
 from funasr.models.frontend.wav_frontend import WavFrontend
 from funasr.tasks.vad import VADTask
-from funasr.utils.timestamp_tools import time_stamp_lfr6
+from funasr.utils.timestamp_tools import time_stamp_lfr6, time_stamp_lfr6_pl
 from funasr.bin.punctuation_infer import Text2Punc
 
 header_colors = '\033[95m'
@@ -234,6 +235,10 @@ class Speech2Text:
         decoder_outs = self.asr_model.cal_decoder_with_predictor(enc, enc_len, pre_acoustic_embeds, pre_token_length)
         decoder_out, ys_pad_lens = decoder_outs[0], decoder_outs[1]
 
+        if isinstance(self.asr_model, BiCifParaformer):
+            _, _, us_alphas, us_cif_peak = self.asr_model.calc_predictor_timestamp(enc, enc_len,
+                                                                                   pre_token_length)  # test no bias cif2
+
         results = []
         b, n, d = decoder_out.size()
         for i in range(b):
@@ -276,9 +281,12 @@ class Speech2Text:
                 else:
                     text = None
 
-                time_stamp = time_stamp_lfr6(alphas[i:i+1,], enc_len[i:i+1,], token, begin_time, end_time)
-    
-                results.append((text, token, token_int, time_stamp, enc_len_batch_total, lfr_factor))
+                if isinstance(self.asr_model, BiCifParaformer):
+                    timestamp = time_stamp_lfr6_pl(us_alphas[i], us_cif_peak[i], copy.copy(token), begin_time, end_time)
+                    results.append((text, token, token_int, timestamp, enc_len_batch_total, lfr_factor))
+                else:
+                    time_stamp = time_stamp_lfr6(alphas[i:i + 1, ], enc_len[i:i + 1, ], copy.copy(token), begin_time, end_time)
+                    results.append((text, token, token_int, time_stamp, enc_len_batch_total, lfr_factor))
 
         # assert check_return_type(results)
         return results

funasr/models/e2e_asr_paraformer.py

@@ -8,6 +8,8 @@ from typing import Tuple
 from typing import Union
 
 import torch
+import random
+import numpy as np
 from typeguard import check_argument_types
 
 from funasr.layers.abs_normalize import AbsNormalize
@@ -24,7 +26,7 @@ from funasr.models.predictor.cif import mae_loss
 from funasr.models.preencoder.abs_preencoder import AbsPreEncoder
 from funasr.models.specaug.abs_specaug import AbsSpecAug
 from funasr.modules.add_sos_eos import add_sos_eos
-from funasr.modules.nets_utils import make_pad_mask
+from funasr.modules.nets_utils import make_pad_mask, pad_list
 from funasr.modules.nets_utils import th_accuracy
 from funasr.torch_utils.device_funcs import force_gatherable
 from funasr.train.abs_espnet_model import AbsESPnetModel
@@ -824,7 +826,10 @@ class ParaformerBert(Paraformer):
 
 class BiCifParaformer(Paraformer):
 
-    """CTC-attention hybrid Encoder-Decoder model"""
+    """
+    Paraformer model with an extra cif predictor
+    to conduct accurate timestamp prediction
+    """
 
     def __init__(
         self,
@@ -891,7 +896,7 @@ class BiCifParaformer(Paraformer):
         )
         assert isinstance(self.predictor, CifPredictorV3), "BiCifParaformer should use CIFPredictorV3"
 
-    def _calc_att_loss(
+    def _calc_pre2_loss(
             self,
             encoder_out: torch.Tensor,
             encoder_out_lens: torch.Tensor,
@@ -903,47 +908,12 @@ class BiCifParaformer(Paraformer):
         if self.predictor_bias == 1:
             _, ys_pad = add_sos_eos(ys_pad, self.sos, self.eos, self.ignore_id)
             ys_pad_lens = ys_pad_lens + self.predictor_bias
-        pre_acoustic_embeds, pre_token_length, _, pre_peak_index, pre_token_length2 = self.predictor(encoder_out, ys_pad, encoder_out_mask,
-                                                                                  ignore_id=self.ignore_id)
-
-        # 0. sampler
-        decoder_out_1st = None
-        if self.sampling_ratio > 0.0:
-            if self.step_cur < 2:
-                logging.info("enable sampler in paraformer, sampling_ratio: {}".format(self.sampling_ratio))
-            sematic_embeds, decoder_out_1st = self.sampler(encoder_out, encoder_out_lens, ys_pad, ys_pad_lens,
-                                                           pre_acoustic_embeds)
-        else:
-            if self.step_cur < 2:
-                logging.info("disable sampler in paraformer, sampling_ratio: {}".format(self.sampling_ratio))
-            sematic_embeds = pre_acoustic_embeds
-
-        # 1. Forward decoder
-        decoder_outs = self.decoder(
-            encoder_out, encoder_out_lens, sematic_embeds, ys_pad_lens
-        )
-        decoder_out, _ = decoder_outs[0], decoder_outs[1]
-
-        if decoder_out_1st is None:
-            decoder_out_1st = decoder_out
-        # 2. Compute attention loss
-        loss_att = self.criterion_att(decoder_out, ys_pad)
-        acc_att = th_accuracy(
-            decoder_out_1st.view(-1, self.vocab_size),
-            ys_pad,
-            ignore_label=self.ignore_id,
-        )
-        loss_pre = self.criterion_pre(ys_pad_lens.type_as(pre_token_length), pre_token_length)
-        loss_pre2 = self.criterion_pre(ys_pad_lens.type_as(pre_token_length), pre_token_length2)
+        _, _, _, _, pre_token_length2 = self.predictor(encoder_out, ys_pad, encoder_out_mask, ignore_id=self.ignore_id)
 
-        # Compute cer/wer using attention-decoder
-        if self.training or self.error_calculator is None:
-            cer_att, wer_att = None, None
-        else:
-            ys_hat = decoder_out_1st.argmax(dim=-1)
-            cer_att, wer_att = self.error_calculator(ys_hat.cpu(), ys_pad.cpu())
+        # loss_pre = self.criterion_pre(ys_pad_lens.type_as(pre_token_length), pre_token_length)
+        loss_pre2 = self.criterion_pre(ys_pad_lens.type_as(pre_token_length2), pre_token_length2)
 
-        return loss_att, acc_att, cer_att, wer_att, loss_pre, loss_pre2
+        return loss_pre2
     
     def calc_predictor(self, encoder_out, encoder_out_lens):
 
@@ -956,8 +926,10 @@ class BiCifParaformer(Paraformer):
     def calc_predictor_timestamp(self, encoder_out, encoder_out_lens, token_num):
         encoder_out_mask = (~make_pad_mask(encoder_out_lens, maxlen=encoder_out.size(1))[:, None, :]).to(
             encoder_out.device)
-        ds_alphas, ds_cif_peak, us_alphas, us_cif_peak = self.predictor.get_upsample_timestamp(encoder_out, None, encoder_out_mask, token_num=token_num,
-                                                                                  ignore_id=self.ignore_id)
+        ds_alphas, ds_cif_peak, us_alphas, us_cif_peak = self.predictor.get_upsample_timestamp(encoder_out,
+                                                                                               encoder_out_mask,
+                                                                                               token_num)
+
         import pdb; pdb.set_trace()
         return ds_alphas, ds_cif_peak, us_alphas, us_cif_peak
 
@@ -992,72 +964,16 @@ class BiCifParaformer(Paraformer):
 
         # 1. Encoder
         encoder_out, encoder_out_lens = self.encode(speech, speech_lengths)
-        intermediate_outs = None
-        if isinstance(encoder_out, tuple):
-            intermediate_outs = encoder_out[1]
-            encoder_out = encoder_out[0]
 
-        loss_att, acc_att, cer_att, wer_att = None, None, None, None
-        loss_ctc, cer_ctc = None, None
-        loss_pre = None
         stats = dict()
 
-        # 1. CTC branch
-        if self.ctc_weight != 0.0:
-            loss_ctc, cer_ctc = self._calc_ctc_loss(
-                encoder_out, encoder_out_lens, text, text_lengths
-            )
-
-            # Collect CTC branch stats
-            stats["loss_ctc"] = loss_ctc.detach() if loss_ctc is not None else None
-            stats["cer_ctc"] = cer_ctc
-
-        # Intermediate CTC (optional)
-        loss_interctc = 0.0
-        if self.interctc_weight != 0.0 and intermediate_outs is not None:
-            for layer_idx, intermediate_out in intermediate_outs:
-                # we assume intermediate_out has the same length & padding
-                # as those of encoder_out
-                loss_ic, cer_ic = self._calc_ctc_loss(
-                    intermediate_out, encoder_out_lens, text, text_lengths
-                )
-                loss_interctc = loss_interctc + loss_ic
-
-                # Collect Intermedaite CTC stats
-                stats["loss_interctc_layer{}".format(layer_idx)] = (
-                    loss_ic.detach() if loss_ic is not None else None
-                )
-                stats["cer_interctc_layer{}".format(layer_idx)] = cer_ic
-
-            loss_interctc = loss_interctc / len(intermediate_outs)
-
-            # calculate whole encoder loss
-            loss_ctc = (
-                               1 - self.interctc_weight
-                       ) * loss_ctc + self.interctc_weight * loss_interctc
-
-        # 2b. Attention decoder branch
-        if self.ctc_weight != 1.0:
-            loss_att, acc_att, cer_att, wer_att, loss_pre, loss_pre2 = self._calc_att_loss(
-                encoder_out, encoder_out_lens, text, text_lengths
-            )
-
-        # 3. CTC-Att loss definition
-        if self.ctc_weight == 0.0:
-            loss = loss_att + loss_pre * self.predictor_weight + loss_pre2 * self.predictor_weight
-        elif self.ctc_weight == 1.0:
-            loss = loss_ctc
-        else:
-            loss = self.ctc_weight * loss_ctc + (1 - self.ctc_weight) * loss_att + loss_pre * self.predictor_weight + loss_pre2 * self.predictor_weight
+        loss_pre2 = self._calc_pre2_loss(
+            encoder_out, encoder_out_lens, text, text_lengths
+        )
 
-        # Collect Attn branch stats
-        stats["loss_att"] = loss_att.detach() if loss_att is not None else None
-        stats["acc"] = acc_att
-        stats["cer"] = cer_att
-        stats["wer"] = wer_att
-        stats["loss_pre"] = loss_pre.detach().cpu() if loss_pre is not None else None
-        stats["loss_pre2"] = loss_pre2.detach().cpu() if loss_pre is not None else None
+        loss = loss_pre2
 
+        stats["loss_pre2"] = loss_pre2.detach().cpu()
         stats["loss"] = torch.clone(loss.detach())
 
         # force_gatherable: to-device and to-tensor if scalar for DataParallel

funasr/models/predictor/cif.py

@@ -544,9 +544,8 @@ class CifPredictorV3(nn.Module):
             token_num_int = torch.max(token_num).type(torch.int32).item()
             acoustic_embeds = acoustic_embeds[:, :token_num_int, :]
         return acoustic_embeds, token_num, alphas, cif_peak, token_num2
-    
-    def get_upsample_timestamp(self, hidden, target_label=None, mask=None, ignore_id=-1, mask_chunk_predictor=None,
-                target_label_length=None, token_num=None):
+
+    def get_upsample_timestamp(self, hidden, mask=None, token_num=None):
         h = hidden
         b = hidden.shape[0]
         context = h.transpose(1, 2)

funasr/utils/timestamp_tools.py

@@ -86,14 +86,52 @@ def time_stamp_lfr6(alphas: torch.Tensor, speech_lengths: torch.Tensor, raw_text
     else:
         return time_stamp_list
 
-
-def time_stamp_lfr6_advance(tst: List, text: str):
-    # advanced timestamp prediction for BiCIF_Paraformer using upsampled alphas
-    ds_alphas, ds_cif_peak, us_alphas, us_cif_peak = tst
-    if text.endswith('</s>'):
-        text = text[:-4]
+def time_stamp_lfr6_pl(us_alphas, us_cif_peak, char_list, begin_time=0.0, end_time=None):
+    START_END_THRESHOLD = 5
+    TIME_RATE = 10.0 * 6 / 1000 / 3  #  3 times upsampled
+    if len(us_alphas.shape) == 3:
+        alphas, cif_peak = us_alphas[0], us_cif_peak[0]  # support inference batch_size=1 only
     else:
-        text = text[:-1]
-        logging.warning("found text does not end with </s>")
-    assert int(ds_alphas.sum() + 1e-4) - 1 == len(text)
-    
+        alphas, cif_peak = us_alphas, us_cif_peak
+    num_frames = cif_peak.shape[0]
+    if char_list[-1] == '</s>':
+        char_list = char_list[:-1]
+    # char_list = [i for i in text]
+    timestamp_list = []
+    # for bicif model trained with large data, cif2 actually fires when a character starts
+    # so treat the frames between two peaks as the duration of the former token
+    fire_place = torch.where(cif_peak>1.0-1e-4)[0].cpu().numpy() - 1.5
+    num_peak = len(fire_place)
+    assert num_peak == len(char_list) + 1 # number of peaks is supposed to be number of tokens + 1
+    # begin silence
+    if fire_place[0] > START_END_THRESHOLD:
+        char_list.insert(0, '<sil>')
+        timestamp_list.append([0.0, fire_place[0]*TIME_RATE])
+    # tokens timestamp
+    for i in range(len(fire_place)-1):
+        # the peak is always a little ahead of the start time
+        # timestamp_list.append([(fire_place[i]-1.2)*TIME_RATE, fire_place[i+1]*TIME_RATE])
+        timestamp_list.append([(fire_place[i])*TIME_RATE, fire_place[i+1]*TIME_RATE])
+        # cut the duration to token and sil of the 0-weight frames last long
+    # tail token and end silence
+    if num_frames - fire_place[-1] > START_END_THRESHOLD:
+        _end = (num_frames + fire_place[-1]) / 2
+        timestamp_list[-1][1] = _end*TIME_RATE
+        timestamp_list.append([_end*TIME_RATE, num_frames*TIME_RATE])
+        char_list.append("<sil>")
+    else:
+        timestamp_list[-1][1] = num_frames*TIME_RATE
+    if begin_time:  # add offset time in model with vad
+        for i in range(len(timestamp_list)):
+            timestamp_list[i][0] = timestamp_list[i][0] + begin_time / 1000.0
+            timestamp_list[i][1] = timestamp_list[i][1] + begin_time / 1000.0
+    res_txt = ""
+    for char, timestamp in zip(char_list, timestamp_list):
+        res_txt += "{} {} {};".format(char, timestamp[0], timestamp[1])
+    logging.warning(res_txt)  # for test
+    res = []
+    for char, timestamp in zip(char_list, timestamp_list):
+        if char != '<sil>':
+            res.append([int(timestamp[0] * 1000), int(timestamp[1] * 1000)])
+    return res
+