From 1028a8a036cabd6091fc1a040bbddd565fd3e911 Mon Sep 17 00:00:00 2001
From: 游雁 <zhifu.gzf@alibaba-inc.com>
Date: 星期三, 10 一月 2024 17:42:53 +0800
Subject: [PATCH] funasr1.0 paraformer_streaming WavFrontendOnline
---
funasr/bin/inference.py | 5
funasr/frontends/wav_frontend.py | 116 ++++++++++---------
/dev/null | 69 -----------
examples/industrial_data_pretraining/paraformer_streaming/finetune.sh | 14 ++
examples/industrial_data_pretraining/paraformer_streaming/README_zh.md | 42 +++++++
funasr/utils/load_utils.py | 6
examples/industrial_data_pretraining/paraformer_streaming/demo.py | 38 ++++++
examples/industrial_data_pretraining/paraformer_streaming/infer.sh | 11 +
8 files changed, 172 insertions(+), 129 deletions(-)
diff --git a/examples/industrial_data_pretraining/paraformer_streaming/README_zh.md b/examples/industrial_data_pretraining/paraformer_streaming/README_zh.md
new file mode 100644
index 0000000..8ddb202
--- /dev/null
+++ b/examples/industrial_data_pretraining/paraformer_streaming/README_zh.md
@@ -0,0 +1,42 @@
+(绠�浣撲腑鏂噟[English](./README.md))
+
+# 璇煶璇嗗埆
+
+> **娉ㄦ剰**:
+> pipeline 鏀寔 [modelscope妯″瀷浠撳簱](https://alibaba-damo-academy.github.io/FunASR/en/model_zoo/modelscope_models.html#pretrained-models-on-modelscope) 涓殑鎵�鏈夋ā鍨嬭繘琛屾帹鐞嗗拰寰皟銆傝繖閲屾垜浠互鍏稿瀷妯″瀷浣滀负绀轰緥鏉ユ紨绀轰娇鐢ㄦ柟娉曘��
+
+## 鎺ㄧ悊
+
+### 蹇�熶娇鐢�
+#### [Paraformer 妯″瀷](https://www.modelscope.cn/models/damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch/summary)
+```python
+from funasr import AutoModel
+
+model = AutoModel(model="/Users/zhifu/Downloads/modelscope_models/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch")
+
+res = model(input="/Users/zhifu/Downloads/modelscope_models/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch/example/asr_example.wav")
+print(res)
+```
+
+### API鎺ュ彛璇存槑
+#### AutoModel 瀹氫箟
+- `model`: [妯″瀷浠撳簱](https://alibaba-damo-academy.github.io/FunASR/en/model_zoo/modelscope_models.html#pretrained-models-on-modelscope) 涓殑妯″瀷鍚嶇О锛屾垨鏈湴纾佺洏涓殑妯″瀷璺緞
+- `device`: `cuda`锛堥粯璁わ級锛屼娇鐢� GPU 杩涜鎺ㄧ悊銆傚鏋滀负`cpu`锛屽垯浣跨敤 CPU 杩涜鎺ㄧ悊
+- `ncpu`: `None` 锛堥粯璁わ級锛岃缃敤浜� CPU 鍐呴儴鎿嶄綔骞惰鎬х殑绾跨▼鏁�
+- `output_dir`: `None` 锛堥粯璁わ級锛屽鏋滆缃紝杈撳嚭缁撴灉鐨勮緭鍑鸿矾寰�
+- `batch_size`: `1` 锛堥粯璁わ級锛岃В鐮佹椂鐨勬壒澶勭悊澶у皬
+#### AutoModel 鎺ㄧ悊
+- `input`: 瑕佽В鐮佺殑杈撳叆锛屽彲浠ユ槸锛�
+ - wav鏂囦欢璺緞, 渚嬪: asr_example.wav
+ - pcm鏂囦欢璺緞, 渚嬪: asr_example.pcm锛屾鏃堕渶瑕佹寚瀹氶煶棰戦噰鏍风巼fs锛堥粯璁や负16000锛�
+ - 闊抽瀛楄妭鏁版祦锛屼緥濡傦細楹﹀厠椋庣殑瀛楄妭鏁版暟鎹�
+ - wav.scp锛宬aldi 鏍峰紡鐨� wav 鍒楄〃 (`wav_id \t wav_path`), 渚嬪:
+ ```text
+ asr_example1 ./audios/asr_example1.wav
+ asr_example2 ./audios/asr_example2.wav
+ ```
+ 鍦ㄨ繖绉嶈緭鍏� `wav.scp` 鐨勬儏鍐典笅锛屽繀椤昏缃� `output_dir` 浠ヤ繚瀛樿緭鍑虹粨鏋�
+ - 闊抽閲囨牱鐐癸紝渚嬪锛歚audio, rate = soundfile.read("asr_example_zh.wav")`, 鏁版嵁绫诲瀷涓� numpy.ndarray銆傛敮鎸乥atch杈撳叆锛岀被鍨嬩负list锛�
+ ```[audio_sample1, audio_sample2, ..., audio_sampleN]```
+ - fbank杈撳叆锛屾敮鎸佺粍batch銆俿hape涓篬batch, frames, dim]锛岀被鍨嬩负torch.Tensor锛屼緥濡�
+- `output_dir`: None 锛堥粯璁わ級锛屽鏋滆缃紝杈撳嚭缁撴灉鐨勮緭鍑鸿矾寰�
diff --git a/examples/industrial_data_pretraining/paraformer_streaming/demo.py b/examples/industrial_data_pretraining/paraformer_streaming/demo.py
new file mode 100644
index 0000000..0036e77
--- /dev/null
+++ b/examples/industrial_data_pretraining/paraformer_streaming/demo.py
@@ -0,0 +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)
+
+# from funasr import AutoModel
+#
+# model = AutoModel(model="damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch", model_revison="v2.0.0")
+#
+# res = model(input="https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_zh.wav")
+# print(res)
+
+
+from funasr import AutoFrontend
+
+frontend = AutoFrontend(model="/Users/zhifu/Downloads/modelscope_models/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-online", model_revison="v2.0.0")
+
+
+
+import soundfile
+speech, sample_rate = soundfile.read("/Users/zhifu/Downloads/modelscope_models/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-online/example/asr_example.wav")
+
+chunk_size = [0, 10, 5] #[0, 10, 5] 600ms, [0, 8, 4] 480ms
+chunk_stride = chunk_size[1] * 960 # 600ms銆�480ms
+# first chunk, 600ms
+
+cache = {}
+
+for i in range(int(len((speech)-1)/chunk_stride+1)):
+ speech_chunk = speech[i*chunk_stride:(i+1)*chunk_stride]
+ fbanks = frontend(input=speech_chunk,
+ batch_size=2,
+ cache=cache)
+
+
+# for batch_idx, fbank_dict in enumerate(fbanks):
+# res = model(**fbank_dict)
+# print(res)
\ No newline at end of file
diff --git a/examples/industrial_data_pretraining/paraformer_streaming/finetune.sh b/examples/industrial_data_pretraining/paraformer_streaming/finetune.sh
new file mode 100644
index 0000000..6dca09f
--- /dev/null
+++ b/examples/industrial_data_pretraining/paraformer_streaming/finetune.sh
@@ -0,0 +1,14 @@
+
+# download model
+local_path_root=../modelscope_models
+mkdir -p ${local_path_root}
+local_path=${local_path_root}/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch
+git clone https://www.modelscope.cn/damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch.git ${local_path}
+
+
+python funasr/bin/train.py \
++model="../modelscope_models/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch" \
++token_list="../modelscope_models/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch/tokens.txt" \
++train_data_set_list="data/list/audio_datasets.jsonl" \
++output_dir="outputs/debug/ckpt/funasr2/exp2" \
++device="cpu"
\ No newline at end of file
diff --git a/examples/industrial_data_pretraining/paraformer_streaming/infer.sh b/examples/industrial_data_pretraining/paraformer_streaming/infer.sh
new file mode 100644
index 0000000..9436628
--- /dev/null
+++ b/examples/industrial_data_pretraining/paraformer_streaming/infer.sh
@@ -0,0 +1,11 @@
+
+model="damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch"
+model_revision="v2.0.0"
+
+python funasr/bin/inference.py \
++model=${model} \
++model_revision=${model_revision} \
++input="https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_zh.wav" \
++output_dir="./outputs/debug" \
++device="cpu" \
+
diff --git a/funasr/bin/inference.py b/funasr/bin/inference.py
index dedaf7d..c4ff69b 100644
--- a/funasr/bin/inference.py
+++ b/funasr/bin/inference.py
@@ -391,7 +391,10 @@
frontend = frontend_class(**kwargs["frontend_conf"])
self.frontend = frontend
+ if "frontend" in kwargs:
+ del kwargs["frontend"]
self.kwargs = kwargs
+
def __call__(self, input, input_len=None, kwargs=None, **cfg):
@@ -423,7 +426,7 @@
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=self.frontend)
+ frontend=self.frontend, **kwargs)
time3 = time.perf_counter()
meta_data["extract_feat"] = f"{time3 - time2:0.3f}"
meta_data["batch_data_time"] = speech_lengths.sum().item() * self.frontend.frame_shift * self.frontend.lfr_n / 1000
diff --git a/funasr/frontends/wav_frontend.py b/funasr/frontends/wav_frontend.py
index 746bf82..fe22335 100644
--- a/funasr/frontends/wav_frontend.py
+++ b/funasr/frontends/wav_frontend.py
@@ -1,7 +1,7 @@
# Copyright (c) Alibaba, Inc. and its affiliates.
# Part of the implementation is borrowed from espnet/espnet.
from typing import Tuple
-
+import copy
import numpy as np
import torch
import torch.nn as nn
@@ -119,7 +119,9 @@
def forward(
self,
input: torch.Tensor,
- input_lengths) -> Tuple[torch.Tensor, torch.Tensor]:
+ input_lengths,
+ **kwargs,
+ ) -> Tuple[torch.Tensor, torch.Tensor]:
batch_size = input.size(0)
feats = []
feats_lens = []
@@ -249,13 +251,13 @@
self.dither = dither
self.snip_edges = snip_edges
self.upsacle_samples = upsacle_samples
- self.waveforms = None
- self.reserve_waveforms = None
- self.fbanks = None
- self.fbanks_lens = None
+ # self.waveforms = None
+ # self.reserve_waveforms = None
+ # self.fbanks = None
+ # self.fbanks_lens = None
self.cmvn = None if self.cmvn_file is None else load_cmvn(self.cmvn_file)
- self.input_cache = None
- self.lfr_splice_cache = []
+ # self.input_cache = None
+ # self.lfr_splice_cache = []
def output_size(self) -> int:
return self.n_mels * self.lfr_m
@@ -278,9 +280,6 @@
return inputs.type(torch.float32)
@staticmethod
- # inputs tensor has catted the cache tensor
- # def apply_lfr(inputs: torch.Tensor, lfr_m: int, lfr_n: int, inputs_lfr_cache: torch.Tensor = None,
- # is_final: bool = False) -> Tuple[torch.Tensor, torch.Tensor, int]:
def apply_lfr(inputs: torch.Tensor, lfr_m: int, lfr_n: int, is_final: bool = False) -> Tuple[
torch.Tensor, torch.Tensor, int]:
"""
@@ -319,15 +318,16 @@
def forward_fbank(
self,
input: torch.Tensor,
- input_lengths: torch.Tensor
+ input_lengths: torch.Tensor,
+ cache: dict = {},
+ **kwargs,
) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
batch_size = input.size(0)
- if self.input_cache is None:
- self.input_cache = torch.empty(0)
- input = torch.cat((self.input_cache, input), dim=1)
+
+ input = torch.cat((cache["input_cache"], input), dim=1)
frame_num = self.compute_frame_num(input.shape[-1], self.frame_sample_length, self.frame_shift_sample_length)
# update self.in_cache
- self.input_cache = input[:, -(input.shape[-1] - frame_num * self.frame_shift_sample_length):]
+ cache["input_cache"] = input[:, -(input.shape[-1] - frame_num * self.frame_shift_sample_length):]
waveforms = torch.empty(0)
feats_pad = torch.empty(0)
feats_lens = torch.empty(0)
@@ -360,20 +360,19 @@
feats_pad = pad_sequence(feats,
batch_first=True,
padding_value=0.0)
- self.fbanks = feats_pad
- import copy
- self.fbanks_lens = copy.deepcopy(feats_lens)
+ cache["fbanks"] = feats_pad
+ cache["fbanks_lens"]= copy.deepcopy(feats_lens)
return waveforms, feats_pad, feats_lens
- def get_fbank(self) -> Tuple[torch.Tensor, torch.Tensor]:
- return self.fbanks, self.fbanks_lens
def forward_lfr_cmvn(
self,
input: torch.Tensor,
input_lengths: torch.Tensor,
- is_final: bool = False
- ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+ is_final: bool = False,
+ cache: dict = {},
+ **kwargs,
+ ):
batch_size = input.size(0)
feats = []
feats_lens = []
@@ -383,7 +382,7 @@
if self.lfr_m != 1 or self.lfr_n != 1:
# update self.lfr_splice_cache in self.apply_lfr
# mat, self.lfr_splice_cache[i], lfr_splice_frame_idx = self.apply_lfr(mat, self.lfr_m, self.lfr_n, self.lfr_splice_cache[i],
- mat, self.lfr_splice_cache[i], lfr_splice_frame_idx = self.apply_lfr(mat, self.lfr_m, self.lfr_n,
+ mat, cache["lfr_splice_cache"][i], lfr_splice_frame_idx = self.apply_lfr(mat, self.lfr_m, self.lfr_n,
is_final)
if self.cmvn_file is not None:
mat = self.apply_cmvn(mat, self.cmvn)
@@ -400,63 +399,68 @@
return feats_pad, feats_lens, lfr_splice_frame_idxs
def forward(
- self, input: torch.Tensor, input_lengths: torch.Tensor, is_final: bool = False, reset: bool = False
- ) -> Tuple[torch.Tensor, torch.Tensor]:
- if reset:
- self.cache_reset()
+ self, input: torch.Tensor, input_lengths: torch.Tensor, cache: dict = {}, **kwargs
+ ):
+ is_final = kwargs.get("is_final", False)
+ reset = kwargs.get("reset", False)
+ if len(cache) == 0 or reset:
+ self.init_cache(cache)
+
batch_size = input.shape[0]
assert batch_size == 1, 'we support to extract feature online only when the batch size is equal to 1 now'
- waveforms, feats, feats_lengths = self.forward_fbank(input, input_lengths) # input shape: B T D
+
+ waveforms, feats, feats_lengths = self.forward_fbank(input, input_lengths, cache=cache) # input shape: B T D
+
if feats.shape[0]:
- # if self.reserve_waveforms is None and self.lfr_m > 1:
- # self.reserve_waveforms = waveforms[:, :(self.lfr_m - 1) // 2 * self.frame_shift_sample_length]
- self.waveforms = waveforms if self.reserve_waveforms is None else torch.cat(
- (self.reserve_waveforms, waveforms), dim=1)
- if not self.lfr_splice_cache: # 鍒濆鍖杝plice_cache
+
+ cache["waveforms"] = torch.cat((cache["reserve_waveforms"], waveforms), dim=1)
+
+ if not cache["lfr_splice_cache"]: # 鍒濆鍖杝plice_cache
for i in range(batch_size):
- self.lfr_splice_cache.append(feats[i][0, :].unsqueeze(dim=0).repeat((self.lfr_m - 1) // 2, 1))
+ cache["lfr_splice_cache"].append(feats[i][0, :].unsqueeze(dim=0).repeat((self.lfr_m - 1) // 2, 1))
# need the number of the input frames + self.lfr_splice_cache[0].shape[0] is greater than self.lfr_m
- if feats_lengths[0] + self.lfr_splice_cache[0].shape[0] >= self.lfr_m:
- lfr_splice_cache_tensor = torch.stack(self.lfr_splice_cache) # B T D
+ if feats_lengths[0] + cache["lfr_splice_cache"][0].shape[0] >= self.lfr_m:
+ lfr_splice_cache_tensor = torch.stack(cache["lfr_splice_cache"]) # B T D
feats = torch.cat((lfr_splice_cache_tensor, feats), dim=1)
feats_lengths += lfr_splice_cache_tensor[0].shape[0]
frame_from_waveforms = int(
- (self.waveforms.shape[1] - self.frame_sample_length) / self.frame_shift_sample_length + 1)
- minus_frame = (self.lfr_m - 1) // 2 if self.reserve_waveforms is None else 0
- feats, feats_lengths, lfr_splice_frame_idxs = self.forward_lfr_cmvn(feats, feats_lengths, is_final)
+ (cache["waveforms"].shape[1] - self.frame_sample_length) / self.frame_shift_sample_length + 1)
+ minus_frame = (self.lfr_m - 1) // 2 if cache["reserve_waveforms"].numel() == 0 else 0
+ feats, feats_lengths, lfr_splice_frame_idxs = self.forward_lfr_cmvn(feats, feats_lengths, is_final, cache=cache)
if self.lfr_m == 1:
- self.reserve_waveforms = None
+ cache["reserve_waveforms"] = torch.empty(0)
else:
reserve_frame_idx = lfr_splice_frame_idxs[0] - minus_frame
# print('reserve_frame_idx: ' + str(reserve_frame_idx))
# print('frame_frame: ' + str(frame_from_waveforms))
- self.reserve_waveforms = self.waveforms[:, reserve_frame_idx * self.frame_shift_sample_length:frame_from_waveforms * self.frame_shift_sample_length]
+ cache["reserve_waveforms"] = cache["waveforms"][:, reserve_frame_idx * self.frame_shift_sample_length:frame_from_waveforms * self.frame_shift_sample_length]
sample_length = (frame_from_waveforms - 1) * self.frame_shift_sample_length + self.frame_sample_length
- self.waveforms = self.waveforms[:, :sample_length]
+ cache["waveforms"] = cache["waveforms"][:, :sample_length]
else:
# update self.reserve_waveforms and self.lfr_splice_cache
- self.reserve_waveforms = self.waveforms[:,
- :-(self.frame_sample_length - self.frame_shift_sample_length)]
+ cache["reserve_waveforms"] = cache["waveforms"][:, :-(self.frame_sample_length - self.frame_shift_sample_length)]
for i in range(batch_size):
- self.lfr_splice_cache[i] = torch.cat((self.lfr_splice_cache[i], feats[i]), dim=0)
+ cache["lfr_splice_cache"][i] = torch.cat((cache["lfr_splice_cache"][i], feats[i]), dim=0)
return torch.empty(0), feats_lengths
else:
if is_final:
- self.waveforms = waveforms if self.reserve_waveforms is None else self.reserve_waveforms
- feats = torch.stack(self.lfr_splice_cache)
+ cache["waveforms"] = waveforms if cache["reserve_waveforms"].numel() == 0 else cache["reserve_waveforms"]
+ feats = torch.stack(cache["lfr_splice_cache"])
feats_lengths = torch.zeros(batch_size, dtype=torch.int) + feats.shape[1]
- feats, feats_lengths, _ = self.forward_lfr_cmvn(feats, feats_lengths, is_final)
+ feats, feats_lengths, _ = self.forward_lfr_cmvn(feats, feats_lengths, is_final, cache=cache)
if is_final:
- self.cache_reset()
+ self.init_cache(cache)
return feats, feats_lengths
- def get_waveforms(self):
- return self.waveforms
- def cache_reset(self):
- self.reserve_waveforms = None
- self.input_cache = None
- self.lfr_splice_cache = []
+ def init_cache(self, cache: dict = {}):
+ cache["reserve_waveforms"] = torch.empty(0)
+ cache["input_cache"] = torch.empty(0)
+ cache["lfr_splice_cache"] = []
+ cache["waveforms"] = None
+ cache["fbanks"] = None
+ cache["fbanks_lens"] = None
+ return cache
class WavFrontendMel23(nn.Module):
diff --git a/funasr/frontends/wav_frontend_kaldifeat.py b/funasr/frontends/wav_frontend_kaldifeat.py
deleted file mode 100644
index 5372de3..0000000
--- a/funasr/frontends/wav_frontend_kaldifeat.py
+++ /dev/null
@@ -1,69 +0,0 @@
-# Copyright (c) Alibaba, Inc. and its affiliates.
-# Part of the implementation is borrowed from espnet/espnet.
-
-import numpy as np
-import torch
-
-
-def load_cmvn(cmvn_file):
- with open(cmvn_file, 'r', encoding='utf-8') as f:
- lines = f.readlines()
- means_list = []
- vars_list = []
- for i in range(len(lines)):
- line_item = lines[i].split()
- if line_item[0] == '<AddShift>':
- line_item = lines[i + 1].split()
- if line_item[0] == '<LearnRateCoef>':
- add_shift_line = line_item[3:(len(line_item) - 1)]
- means_list = list(add_shift_line)
- continue
- elif line_item[0] == '<Rescale>':
- line_item = lines[i + 1].split()
- if line_item[0] == '<LearnRateCoef>':
- rescale_line = line_item[3:(len(line_item) - 1)]
- vars_list = list(rescale_line)
- continue
- means = np.array(means_list).astype(np.float)
- vars = np.array(vars_list).astype(np.float)
- cmvn = np.array([means, vars])
- cmvn = torch.as_tensor(cmvn)
- return cmvn
-
-
-def apply_cmvn(inputs, cmvn_file): # noqa
- """
- Apply CMVN with mvn data
- """
-
- device = inputs.device
- dtype = inputs.dtype
- frame, dim = inputs.shape
-
- cmvn = load_cmvn(cmvn_file)
- means = np.tile(cmvn[0:1, :dim], (frame, 1))
- vars = np.tile(cmvn[1:2, :dim], (frame, 1))
- inputs += torch.from_numpy(means).type(dtype).to(device)
- inputs *= torch.from_numpy(vars).type(dtype).to(device)
-
- return inputs.type(torch.float32)
-
-
-def apply_lfr(inputs, lfr_m, lfr_n):
- LFR_inputs = []
- T = inputs.shape[0]
- T_lfr = int(np.ceil(T / lfr_n))
- left_padding = inputs[0].repeat((lfr_m - 1) // 2, 1)
- inputs = torch.vstack((left_padding, inputs))
- T = T + (lfr_m - 1) // 2
- for i in range(T_lfr):
- if lfr_m <= T - i * lfr_n:
- LFR_inputs.append((inputs[i * lfr_n:i * lfr_n + lfr_m]).view(1, -1))
- else: # process last LFR frame
- num_padding = lfr_m - (T - i * lfr_n)
- frame = (inputs[i * lfr_n:]).view(-1)
- for _ in range(num_padding):
- frame = torch.hstack((frame, inputs[-1]))
- LFR_inputs.append(frame)
- LFR_outputs = torch.vstack(LFR_inputs)
- return LFR_outputs.type(torch.float32)
diff --git a/funasr/utils/load_utils.py b/funasr/utils/load_utils.py
index c82987f..39b708a 100644
--- a/funasr/utils/load_utils.py
+++ b/funasr/utils/load_utils.py
@@ -68,7 +68,7 @@
array = np.frombuffer((middle_data.astype(dtype) - offset) / abs_max, dtype=np.float32)
return array
-def extract_fbank(data, data_len = None, data_type: str="sound", frontend=None):
+def extract_fbank(data, data_len = None, data_type: str="sound", frontend=None, **kwargs):
# import pdb;
# pdb.set_trace()
if isinstance(data, np.ndarray):
@@ -83,7 +83,7 @@
elif isinstance(data, (list, tuple)):
data_list, data_len = [], []
for data_i in data:
- if isinstance(data, np.ndarray):
+ if isinstance(data_i, np.ndarray):
data_i = torch.from_numpy(data_i)
data_list.append(data_i)
data_len.append(data_i.shape[0])
@@ -91,7 +91,7 @@
# import pdb;
# pdb.set_trace()
# if data_type == "sound":
- data, data_len = frontend(data, data_len)
+ data, data_len = frontend(data, data_len, **kwargs)
if isinstance(data_len, (list, tuple)):
data_len = torch.tensor([data_len])
--
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