From 9b4e9cc8a0311e5243d69b73ed073e7ea441982e Mon Sep 17 00:00:00 2001
From: 游雁 <zhifu.gzf@alibaba-inc.com>
Date: 星期三, 27 三月 2024 16:05:29 +0800
Subject: [PATCH] train update
---
funasr/models/monotonic_aligner/model.py | 44 ++++++++++++++++++++++++++++----------------
1 files changed, 28 insertions(+), 16 deletions(-)
diff --git a/funasr/models/monotonic_aligner/model.py b/funasr/models/monotonic_aligner/model.py
index 62899f2..718923b 100644
--- a/funasr/models/monotonic_aligner/model.py
+++ b/funasr/models/monotonic_aligner/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 time
import copy
import torch
@@ -5,6 +10,7 @@
from typing import Union, Dict, List, Tuple, Optional
from funasr.register import tables
+from funasr.models.ctc.ctc import CTC
from funasr.utils import postprocess_utils
from funasr.utils.datadir_writer import DatadirWriter
from funasr.models.paraformer.cif_predictor import mae_loss
@@ -12,10 +18,10 @@
from funasr.models.transformer.utils.add_sos_eos import add_sos_eos
from funasr.models.transformer.utils.nets_utils import make_pad_mask
from funasr.utils.timestamp_tools import ts_prediction_lfr6_standard
-from funasr.utils.load_utils import load_audio_and_text_image_video, extract_fbank, load_audio_and_text_image_video
+from funasr.utils.load_utils import load_audio_text_image_video, extract_fbank
-@tables.register("model_classes", "monotonicaligner")
+@tables.register("model_classes", "MonotonicAligner")
class MonotonicAligner(torch.nn.Module):
"""
Author: Speech Lab of DAMO Academy, Alibaba Group
@@ -40,15 +46,15 @@
super().__init__()
if specaug is not None:
- specaug_class = tables.specaug_classes.get(specaug.lower())
+ specaug_class = tables.specaug_classes.get(specaug)
specaug = specaug_class(**specaug_conf)
if normalize is not None:
- normalize_class = tables.normalize_classes.get(normalize.lower())
+ normalize_class = tables.normalize_classes.get(normalize)
normalize = normalize_class(**normalize_conf)
- encoder_class = tables.encoder_classes.get(encoder.lower())
+ encoder_class = tables.encoder_classes.get(encoder)
encoder = encoder_class(input_size=input_size, **encoder_conf)
encoder_output_size = encoder.output_size()
- predictor_class = tables.predictor_classes.get(predictor.lower())
+ predictor_class = tables.predictor_classes.get(predictor)
predictor = predictor_class(**predictor_conf)
self.specaug = specaug
self.normalize = normalize
@@ -142,7 +148,7 @@
return encoder_out, encoder_out_lens
- def generate(self,
+ def inference(self,
data_in,
data_lengths=None,
key: list=None,
@@ -153,7 +159,7 @@
meta_data = {}
# extract fbank feats
time1 = time.perf_counter()
- audio_list, text_token_int_list = load_audio_and_text_image_video(data_in,
+ audio_list, text_token_int_list = load_audio_text_image_video(data_in,
fs=frontend.fs,
audio_fs=kwargs.get("fs", 16000),
data_type=kwargs.get("data_type", "sound"),
@@ -165,7 +171,8 @@
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)
@@ -176,9 +183,11 @@
results = []
ibest_writer = None
- if ibest_writer is None and kwargs.get("output_dir") is not None:
- writer = DatadirWriter(kwargs.get("output_dir"))
- ibest_writer = writer["tp_res"]
+ if kwargs.get("output_dir") is not None:
+ if not hasattr(self, "writer"):
+ self.writer = DatadirWriter(kwargs.get("output_dir"))
+ ibest_writer = self.writer["tp_res"]
+
for i, (us_alpha, us_peak, token_int) in enumerate(zip(us_alphas, us_peaks, text_token_int_list)):
token = tokenizer.ids2tokens(token_int)
timestamp_str, timestamp = ts_prediction_lfr6_standard(us_alpha[:encoder_out_lens[i] * 3],
@@ -187,9 +196,12 @@
text_postprocessed, time_stamp_postprocessed, _ = postprocess_utils.sentence_postprocess(token, timestamp)
result_i = {"key": key[i], "text": text_postprocessed,
"timestamp": time_stamp_postprocessed,
- }
- # ibest_writer["token"][key[i]] = " ".join(token)
- ibest_writer["timestamp_list"][key[i]] = time_stamp_postprocessed
- ibest_writer["timestamp_str"][key[i]] = timestamp_str
+ }
results.append(result_i)
+
+ if ibest_writer:
+ # ibest_writer["token"][key[i]] = " ".join(token)
+ ibest_writer["timestamp_list"][key[i]] = time_stamp_postprocessed
+ ibest_writer["timestamp_str"][key[i]] = timestamp_str
+
return results, meta_data
\ No newline at end of file
--
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