https://github.com/desh2608/gmm-hmm-asr
Python implementation of simple GMM and HMM models for isolated digit recognition.
https://github.com/desh2608/gmm-hmm-asr
Last synced: 2 months ago
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Python implementation of simple GMM and HMM models for isolated digit recognition.
- Host: GitHub
- URL: https://github.com/desh2608/gmm-hmm-asr
- Owner: desh2608
- Created: 2019-03-28T22:57:45.000Z (about 6 years ago)
- Default Branch: master
- Last Pushed: 2021-02-07T17:03:57.000Z (over 4 years ago)
- Last Synced: 2025-03-18T09:21:27.095Z (3 months ago)
- Language: Python
- Size: 75.9 MB
- Stars: 63
- Watchers: 3
- Forks: 22
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Simple GMM-HMM models for isolated digit recognition
Python implementation of simple GMM and HMM models for isolated digit recognition.
This implementation contains 3 models:
1. Single Gaussian: Each digit is modeled using a single Gaussian with diagonal
covariance.
2. Gaussian Mixture Model (GMM): Each digit is modeled using a mixture of Gaussians,
initialized by perturbing the single Gaussian model.
3. Hidden Markov Model (HMM): Each digit is modeled by an HMM consisting of N states,
where the emission probability of each state is a single Gaussian with diagonal covariance.
**Disclaimer:** This is an educational implementation and is not expected to be high-performance.
### Installation
To install for usage:
```shell
pip install git+https://github.com/desh2608/gmm-hmm-asr.git
```
To install with tests (for development):
```shell
git clone https://github.com/desh2608/gmm-hmm-asr.git
cd gmm-hmm-asr && pip install -e .
```
### Running the tests
```shell
pytest
```
This will run each of the 3 models end-to-end, and take approximately 2-3 minutes.
### Usage
#### 1. Single Gaussian
To train, first create `train_data` which should be a list of `DataTuple(key,feats,label)` objects.
```python
from gmm_hmm_asr.data import DataTuple
from gmm_hmm_asr.trainers import SingleGaussTrainer
ndim = 40 # dimensionality of features
DIGITS = ['1','2','3','4','5'] # digits to be recognized
sg_model = SingleGaussTrainer(ndim, DIGITS)
sg_model.train(train_data)
```
For prediction, again create a `test_data` list similar to `train_data`.
```python
preds = sg_model.predict(test_data)
y_pred = [pred[0] for pred in preds] # predicted labels
y_ll = [pred[1] for pred in preds] # maximum log-likelihood
```
#### 2. Gaussian Mixture Model
```python
from gmm_hmm_asr.data import DataTuple
from gmm_hmm_asr.trainers import GMMTrainer
ndim = 40 # dimensionality of features
ncomp = 8 # number of Gaussian components
niter = 10 # number of training iterations
DIGITS = ['1','2','3','4','5'] # digits to be recognized
gmm_model = GMMTrainer(ndim, ncomp, DIGITS)
gmm_model.train(train_data, niter)
preds = gmm_model.predict(test_data)
```
#### 3. Hidden Markov Model
```python
from gmm_hmm_asr.data import DataTuple
from gmm_hmm_asr.trainers import HMMTrainer
ndim = 40 # dimensionality of features
nstate = 5 # number of HMM states
niter = 10 # number of training iterations
DIGITS = ['1','2','3','4','5'] # digits to be recognized
hmm_model = GMMTrainer(ndim, nstate, DIGITS)
hmm_model.train(train_data, niter)
preds = hmm_model.predict(test_data)
```
### Issues
If you find any bugs, please raise an Issue or contact `[email protected]`.