From 94de39dde2e616a01683c518023d0fab72b4e103 Mon Sep 17 00:00:00 2001
From: 游雁 <zhifu.gzf@alibaba-inc.com>
Date: 星期一, 19 二月 2024 22:21:50 +0800
Subject: [PATCH] aishell example
---
funasr/models/bicif_paraformer/model.py | 94 ++++++++++++++++++++++++-----------------------
1 files changed, 48 insertions(+), 46 deletions(-)
diff --git a/funasr/models/bicif_paraformer/model.py b/funasr/models/bicif_paraformer/model.py
index aced088..0069b8c 100644
--- a/funasr/models/bicif_paraformer/model.py
+++ b/funasr/models/bicif_paraformer/model.py
@@ -1,37 +1,38 @@
+#!/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 logging
-from typing import Dict
-from typing import List
-from typing import Optional
-from typing import Tuple
-from typing import Union
-import tempfile
-import codecs
-import requests
-import re
import copy
-import torch
-import torch.nn as nn
-import random
-import numpy as np
import time
+import torch
+import logging
+from contextlib import contextmanager
+from distutils.version import LooseVersion
+from typing import Dict, List, Optional, Tuple
-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
-
-from funasr.utils.load_utils import load_audio_and_text_image_video, extract_fbank
-from funasr.utils import postprocess_utils
-from funasr.utils.datadir_writer import DatadirWriter
-from funasr.utils.timestamp_tools import ts_prediction_lfr6_standard
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.model import Paraformer
+from funasr.models.paraformer.search import Hypothesis
+from funasr.train_utils.device_funcs import force_gatherable
+from funasr.models.transformer.utils.add_sos_eos import add_sos_eos
+from funasr.utils.timestamp_tools import ts_prediction_lfr6_standard
+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
+
+
+if LooseVersion(torch.__version__) >= LooseVersion("1.6.0"):
+ from torch.cuda.amp import autocast
+else:
+ # Nothing to do if torch<1.6.0
+ @contextmanager
+ def autocast(enabled=True):
+ yield
+
@tables.register("model_classes", "BiCifParaformer")
class BiCifParaformer(Paraformer):
@@ -216,7 +217,7 @@
return loss, stats, weight
- def generate(self,
+ def inference(self,
data_in,
data_lengths=None,
key: list = None,
@@ -234,25 +235,26 @@
self.nbest = kwargs.get("nbest", 1)
meta_data = {}
- if isinstance(data_in, torch.Tensor): # fbank
- speech, speech_lengths = data_in, data_lengths
- if len(speech.shape) < 3:
- speech = speech[None, :, :]
- if speech_lengths is None:
- speech_lengths = speech.shape[1]
- else:
- # extract fbank feats
- time1 = time.perf_counter()
- audio_sample_list = load_audio_and_text_image_video(data_in, fs=frontend.fs, audio_fs=kwargs.get("fs", 16000))
- time2 = time.perf_counter()
- meta_data["load_data"] = f"{time2 - time1:0.3f}"
- speech, speech_lengths = extract_fbank(audio_sample_list, data_type=kwargs.get("data_type", "sound"),
- frontend=frontend)
- time3 = time.perf_counter()
- meta_data["extract_feat"] = f"{time3 - time2:0.3f}"
- meta_data["batch_data_time"] = speech_lengths.sum().item() * frontend.frame_shift * frontend.lfr_n / 1000
+ # if isinstance(data_in, torch.Tensor): # fbank
+ # speech, speech_lengths = data_in, data_lengths
+ # if len(speech.shape) < 3:
+ # speech = speech[None, :, :]
+ # if speech_lengths is None:
+ # speech_lengths = speech.shape[1]
+ # else:
+ # extract fbank feats
+ time1 = time.perf_counter()
+ audio_sample_list = load_audio_text_image_video(data_in, fs=frontend.fs, audio_fs=kwargs.get("fs", 16000))
+ time2 = time.perf_counter()
+ meta_data["load_data"] = f"{time2 - time1:0.3f}"
+ speech, speech_lengths = extract_fbank(audio_sample_list, data_type=kwargs.get("data_type", "sound"),
+ frontend=frontend)
+ time3 = time.perf_counter()
+ meta_data["extract_feat"] = f"{time3 - time2:0.3f}"
+ meta_data["batch_data_time"] = speech_lengths.sum().item() * frontend.frame_shift * frontend.lfr_n / 1000
- speech.to(device=kwargs["device"]), speech_lengths.to(device=kwargs["device"])
+ speech = speech.to(device=kwargs["device"])
+ speech_lengths = speech_lengths.to(device=kwargs["device"])
# Encoder
encoder_out, encoder_out_lens = self.encode(speech, speech_lengths)
--
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