Converting a Kaldi ASpIRE Chain Time Delay Neural Network (TDNN) Model¶
At the beginning, you should download a pre-trained model for the ASpIRE Chain Time Delay Neural Network (TDNN) from the Kaldi project official website.
Converting an ASpIRE Chain TDNN Model to IR¶
Generate the Intermediate Representation of the model by running Model Optimizer with the following parameters:
mo --input_model exp/chain/tdnn_7b/final.mdl --output output
The IR will have two inputs: input
for data, and ivector
for ivectors.
Example: Running ASpIRE Chain TDNN Model with the Speech Recognition Sample¶
Note
Before you continue with this part of the article, get familiar with the Speech Recognition sample.
In this example, the input data contains one utterance from one speaker.
To run the ASpIRE Chain TDNN Model with Speech Recognition sample, You need to prepare environment. Do it by following the steps below :
Download a Kaldi repository.
Build it by following instructions in
README.md
from the repository.Download the model archive from Kaldi website.
Extract the downloaded model archive to the
egs/aspire/s5
folder of the Kaldi repository.
Once everything has been prepared, you can start a proper run:
Prepare the model for decoding. Refer to the
README.txt
file from the downloaded model archive for instructions.Convert data and ivectors to
.ark
format. Refer to the corresponding sections below for instructions.
Preparing Data¶
If you have a .wav
data file, convert it to the .ark
format using the following command:
<path_to_kaldi_repo>/src/featbin/compute-mfcc-feats --config=<path_to_kaldi_repo>/egs/aspire/s5/conf/mfcc_hires.conf scp:./wav.scp ark,scp:feats.ark,feats.scp
Add the feats.ark
absolute path to feats.scp
to avoid errors in later commands.
Preparing Ivectors¶
Prepare ivectors for the Speech Recognition sample:
Copy the
feats.scp
file to theegs/aspire/s5/
directory of the built Kaldi repository and navigate there:cp feats.scp <path_to_kaldi_repo>/egs/aspire/s5/ cd <path_to_kaldi_repo>/egs/aspire/s5/
Extract ivectors from the data:
./steps/online/nnet2/extract_ivectors_online.sh --nj 1 --ivector_period <max_frame_count_in_utterance> <data folder> exp/tdnn_7b_chain_online/ivector_extractor <ivector folder>
You can simplify the preparation of ivectors for the Speech Recognition sample. To do it, specify the maximum number of frames in utterances as a parameter for
--ivector_period
to get only one ivector per utterance.
To get the maximum number of frames in utterances, use the following command line:
../../../src/featbin/feat-to-len scp:feats.scp ark,t: | cut -d' ' -f 2 - | sort -rn | head -1
As a result, you will find the ivector_online.1.ark
file in <ivector folder>
.
Go to the
<ivector folder>
:cd <ivector folder>
Convert the
ivector_online.1.ark
file to text format, using thecopy-feats
tool. Run the following command:<path_to_kaldi_repo>/src/featbin/copy-feats --binary=False ark:ivector_online.1.ark ark,t:ivector_online.1.ark.txt
For the Speech Recognition sample, the
.ark
file must contain an ivector for each frame. Copy the ivectorframe_count
times by running the below script in the Python command prompt:import subprocess subprocess.run(["<path_to_kaldi_repo>/src/featbin/feat-to-len", "scp:<path_to_kaldi_repo>/egs/aspire/s5/feats.scp", "ark,t:feats_length.txt"]) f = open("ivector_online.1.ark.txt", "r") g = open("ivector_online_ie.ark.txt", "w") length_file = open("feats_length.txt", "r") for line in f: if "[" not in line: for i in range(frame_count): line = line.replace("]", " ") g.write(line) else: g.write(line) frame_count = int(length_file.read().split(" ")[1]) g.write("]") f.close() g.close() length_file.close()
Create an
.ark
file from.txt
:<path_to_kaldi_repo>/src/featbin/copy-feats --binary=True ark,t:ivector_online_ie.ark.txt ark:ivector_online_ie.ark
Running the Speech Recognition Sample¶
Run the Speech Recognition sample with the created ivector .ark
file:
speech_sample -i feats.ark,ivector_online_ie.ark -m final.xml -d CPU -o prediction.ark -cw_l 17 -cw_r 12
Results can be decoded as described in “Use of Sample in Kaldi Speech Recognition Pipeline” in the Speech Recognition Sample description article.