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/seaco_paraformer/model.py | 57 +++++++++++++++++++++++++++------------------------------
1 files changed, 27 insertions(+), 30 deletions(-)
diff --git a/funasr/models/seaco_paraformer/model.py b/funasr/models/seaco_paraformer/model.py
index 2de125a..8b8e97e 100644
--- a/funasr/models/seaco_paraformer/model.py
+++ b/funasr/models/seaco_paraformer/model.py
@@ -1,3 +1,8 @@
+#!/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 os
import re
import time
@@ -8,24 +13,24 @@
import tempfile
import requests
import numpy as np
-from typing import Dict
-from typing import List
-from typing import Tuple
-from typing import Union
-from typing import Optional
+from typing import Dict, Tuple
from contextlib import contextmanager
from distutils.version import LooseVersion
-from funasr.losses.label_smoothing_loss import (
- LabelSmoothingLoss, # noqa: H301
-)
+from funasr.register import tables
+from funasr.utils import postprocess_utils
+from funasr.metrics.compute_acc import th_accuracy
+from funasr.models.paraformer.model import Paraformer
+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.models.bicif_paraformer.model import BiCifParaformer
+from funasr.losses.label_smoothing_loss import LabelSmoothingLoss
+from funasr.utils.timestamp_tools import ts_prediction_lfr6_standard
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.timestamp_tools import ts_prediction_lfr6_standard
-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_text_image_video, extract_fbank
if LooseVersion(torch.__version__) >= LooseVersion("1.6.0"):
@@ -35,13 +40,6 @@
@contextmanager
def autocast(enabled=True):
yield
-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.models.paraformer.model import Paraformer
-from funasr.models.bicif_paraformer.model import BiCifParaformer
-from funasr.register import tables
@tables.register("model_classes", "SeacoParaformer")
@@ -68,7 +66,6 @@
# bias encoder
if self.bias_encoder_type == 'lstm':
- logging.warning("enable bias encoder sampling and contextual training")
self.bias_encoder = torch.nn.LSTM(self.inner_dim,
self.inner_dim,
2,
@@ -81,7 +78,6 @@
self.lstm_proj = None
self.bias_embed = torch.nn.Embedding(self.vocab_size, self.inner_dim)
elif self.bias_encoder_type == 'mean':
- logging.warning("enable bias encoder sampling and contextual training")
self.bias_embed = torch.nn.Embedding(self.vocab_size, self.inner_dim)
else:
logging.error("Unsupport bias encoder type: {}".format(self.bias_encoder_type))
@@ -90,7 +86,7 @@
seaco_decoder = kwargs.get("seaco_decoder", None)
if seaco_decoder is not None:
seaco_decoder_conf = kwargs.get("seaco_decoder_conf")
- seaco_decoder_class = tables.decoder_classes.get(seaco_decoder.lower())
+ seaco_decoder_class = tables.decoder_classes.get(seaco_decoder)
self.seaco_decoder = seaco_decoder_class(
vocab_size=self.vocab_size,
encoder_output_size=self.inner_dim,
@@ -214,7 +210,7 @@
ys_pad_lens,
hw_list,
nfilter=50,
- seaco_weight=1.0):
+ seaco_weight=1.0):
# decoder forward
decoder_out, decoder_hidden, _ = self.decoder(encoder_out, encoder_out_lens, sematic_embeds, ys_pad_lens, return_hidden=True, return_both=True)
decoder_pred = torch.log_softmax(decoder_out, dim=-1)
@@ -256,10 +252,9 @@
dha_output = self.hotword_output_layer(merged) # remove the last token in loss calculation
dha_pred = torch.log_softmax(dha_output, dim=-1)
- # import pdb; pdb.set_trace()
def _merge_res(dec_output, dha_output):
lmbd = torch.Tensor([seaco_weight] * dha_output.shape[0])
- dha_ids = dha_output.max(-1)[-1][0]
+ dha_ids = dha_output.max(-1)[-1]# [0]
dha_mask = (dha_ids == 8377).int().unsqueeze(-1)
a = (1 - lmbd) / lmbd
b = 1 / lmbd
@@ -269,6 +264,7 @@
logits = dec_output * dha_mask + dha_output[:,:,:] * (1-dha_mask)
return logits
merged_pred = _merge_res(decoder_pred, dha_pred)
+ # import pdb; pdb.set_trace()
return merged_pred
else:
return decoder_pred
@@ -306,7 +302,7 @@
return ds_alphas, ds_cif_peak, us_alphas, us_peaks
'''
- def generate(self,
+ def inference(self,
data_in,
data_lengths=None,
key: list = None,
@@ -327,7 +323,7 @@
# 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))
+ 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"),
@@ -337,7 +333,8 @@
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"])
# hotword
self.hotword_list = self.generate_hotwords_list(kwargs.get("hotword", None), tokenizer=tokenizer, frontend=frontend)
@@ -416,12 +413,12 @@
token, timestamp)
result_i = {"key": key[i], "text": text_postprocessed,
- "timestamp": time_stamp_postprocessed,
+ "timestamp": time_stamp_postprocessed, "raw_text": copy.copy(text_postprocessed)
}
if ibest_writer is not None:
ibest_writer["token"][key[i]] = " ".join(token)
- # ibest_writer["text"][key[i]] = text
+ # ibest_writer["raw_text"][key[i]] = text
ibest_writer["timestamp"][key[i]] = time_stamp_postprocessed
ibest_writer["text"][key[i]] = text_postprocessed
else:
--
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