From 3919d7454c070702e94b149e4032e9db08d28fa3 Mon Sep 17 00:00:00 2001
From: zhifu gao <zhifu.gzf@alibaba-inc.com>
Date: 星期一, 22 一月 2024 15:42:45 +0800
Subject: [PATCH] Funasr1.0 (#1279)

---
 funasr/models/paraformer/model.py |   55 +++++++++++++++++++++++++------------------------------
 1 files changed, 25 insertions(+), 30 deletions(-)

diff --git a/funasr/models/paraformer/model.py b/funasr/models/paraformer/model.py
index 2cd9c88..6e422ad 100644
--- a/funasr/models/paraformer/model.py
+++ b/funasr/models/paraformer/model.py
@@ -1,35 +1,30 @@
-import os
-import logging
-from typing import Union, Dict, List, Tuple, Optional
-
-import torch
-import torch.nn as nn
+#!/usr/bin/env python3
+# -*- encoding: utf-8 -*-
+# Copyright FunASR (https://github.com/alibaba-damo-academy/FunASR). All Rights Reserved.
+#  MIT License  (https://opensource.org/licenses/MIT)
 
 import time
-
-from funasr.losses.label_smoothing_loss import (
-    LabelSmoothingLoss,  # noqa: H301
-)
-
-from funasr.models.paraformer.cif_predictor import mae_loss
-
-from funasr.models.transformer.utils.add_sos_eos import add_sos_eos
-from funasr.models.transformer.utils.nets_utils import make_pad_mask, pad_list
-from funasr.metrics.compute_acc import th_accuracy
-from funasr.train_utils.device_funcs import force_gatherable
-
-from funasr.models.paraformer.search import Hypothesis
-
+import torch
+import logging
 from torch.cuda.amp import autocast
+from typing import Union, Dict, List, Tuple, Optional
 
-from funasr.utils.load_utils import load_audio_text_image_video, extract_fbank
-from funasr.utils import postprocess_utils
-from funasr.utils.datadir_writer import DatadirWriter
 from funasr.register import tables
 from funasr.models.ctc.ctc import CTC
+from funasr.utils import postprocess_utils
+from funasr.metrics.compute_acc import th_accuracy
+from funasr.utils.datadir_writer import DatadirWriter
+from funasr.models.paraformer.search import Hypothesis
+from funasr.models.paraformer.cif_predictor import mae_loss
+from funasr.train_utils.device_funcs import force_gatherable
+from funasr.losses.label_smoothing_loss import LabelSmoothingLoss
+from funasr.models.transformer.utils.add_sos_eos import add_sos_eos
+from funasr.models.transformer.utils.nets_utils import make_pad_mask, pad_list
+from funasr.utils.load_utils import load_audio_text_image_video, extract_fbank
+
 
 @tables.register("model_classes", "Paraformer")
-class Paraformer(nn.Module):
+class Paraformer(torch.nn.Module):
     """
     Author: Speech Lab of DAMO Academy, Alibaba Group
     Paraformer: Fast and Accurate Parallel Transformer for Non-autoregressive End-to-End Speech Recognition
@@ -38,7 +33,6 @@
     
     def __init__(
         self,
-        # token_list: Union[Tuple[str, ...], List[str]],
         specaug: Optional[str] = None,
         specaug_conf: Optional[Dict] = None,
         normalize: str = None,
@@ -169,6 +163,7 @@
         self.use_1st_decoder_loss = use_1st_decoder_loss
         self.length_normalized_loss = length_normalized_loss
         self.beam_search = None
+        self.error_calculator = None
     
     def forward(
         self,
@@ -439,7 +434,7 @@
         #         scorer.to(device=kwargs.get("device", "cpu"), dtype=getattr(torch, kwargs.get("dtype", "float32"))).eval()
         self.beam_search = beam_search
         
-    def generate(self,
+    def inference(self,
              data_in,
              data_lengths=None,
              key: list=None,
@@ -456,7 +451,7 @@
             self.nbest = kwargs.get("nbest", 1)
         
         meta_data = {}
-        if isinstance(data_in, torch.Tensor): # fbank
+        if isinstance(data_in, torch.Tensor) and kwargs.get("data_type", "sound") == "fbank": # fbank
             speech, speech_lengths = data_in, data_lengths
             if len(speech.shape) < 3:
                 speech = speech[None, :, :]
@@ -533,9 +528,9 @@
                 if tokenizer is not None:
                     # Change integer-ids to tokens
                     token = tokenizer.ids2tokens(token_int)
-                    text = tokenizer.tokens2text(token)
-                    
-                    text_postprocessed, _ = postprocess_utils.sentence_postprocess(token)
+                    text_postprocessed = tokenizer.tokens2text(token)
+                    if not hasattr(tokenizer, "bpemodel"):
+                        text_postprocessed, _ = postprocess_utils.sentence_postprocess(token)
                     
                     result_i = {"key": key[i], "text": text_postprocessed}
 

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