You can download a pre-trained model for the ASpIRE Chain Time Delay Neural Network (TDNN) from the Kaldi* project official web-site.
Convert ASpIRE Chain TDNN Model to IR
To generate the Intermediate Representation (IR) of the model, run the Model Optimizer with the following parameters:
python3 ./mo_kaldi.py --input_model exp/chain/tdnn_7b/final.mdl --output output
The IR will have two inputs:
input for data and
ivector for ivectors.
Example: Run ASpIRE Chain TDNN Model with the Speech Recognition Sample
These instructions show how to run the converted model with the Speech Recognition sample. In this example, the input data contains one utterance from one speaker.
To follow the steps described below, you must first do the following:
- Download a Kaldi repository.
- Build it using instructions in
README.md in the repository.
- Download the model archive from Kaldi website.
- Extract the downloaded model archive to the
egs/aspire/s5 folder of the Kaldi repository.
To run the ASpIRE Chain TDNN Model with Speech Recognition sample:
- 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.
If you have a
.wav data file, you can convert it to
.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
feats.ark absolute path to
feats.scp to avoid errors in later commands.
To prepare ivectors for the Speech Recognition sample, do the following:
- Copy the
feats.scp file to the
egs/aspire/s5/ directory of the built Kaldi repository and navigate there:
cp feats.scp <path_to_kaldi_repo>/egs/aspire/s5/
- Extract ivectors from the data:
To simplify the preparation of ivectors for the Speech Recognition sample, specify the maximum number of frames in utterances as a parameter for
./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>
--ivector_period to get only one ivector per utterance.
To get the maximum number of frames in utterances, you can 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, in
<ivector folder>, you will find the
- Go to the
- Convert the
ivector_online.1.ark file to text format using the
copy-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. You must copy the ivector
frame_count times. To do this, you can run the following script in the Python* command prompt:
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("]", " ")
frame_count = int(length_file.read().split(" "))
- Create an
.ark file from
<path_to_kaldi_repo>/src/featbin/copy-feats --binary=True ark,t:ivector_online_ie.ark.txt ark:ivector_online_ie.ark
Run the Speech Recognition Sample
Run the Speech Recognition sample with the created ivector
.ark file as follows:
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" chapter in the Speech Recognition Sample description.