alphacep--vosk-api
Paschalis M
120 行
4.4 KiB
Markdown
120 行
4.4 KiB
Markdown
# Vosk API Training
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This directory contains scripts and tools for training speech recognition models using the Kaldi toolkit.
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## Table of Contents
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1. [Overview](#overview)
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2. [Directory Structure](#directory-structure)
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3. [Installation](#installation)
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4. [Training Process](#training-process)
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- [Data Preparation](#data-preparation)
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- [Dictionary Preparation](#dictionary-preparation)
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- [MFCC Feature Extraction](#mfcc-feature-extraction)
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- [Acoustic Model Training](#acoustic-model-training)
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- [TDNN Chain Model Training](#tdnn-chain-model-training)
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- [Decoding](#decoding)
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5. [Results](#results)
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6. [Contributing](#contributing)
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## Overview
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This repository provides tools for training custom speech recognition models using Kaldi. It supports acoustic model training, language model creation, and decoding pipelines.
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## Directory Structure
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```plaintext
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.
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├── cmd.sh # Command configuration for training and decoding
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├── conf/
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│ ├── mfcc.conf # Configuration for MFCC feature extraction
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│ └── online_cmvn.conf # Online Cepstral Mean Variance Normalization (currently empty)
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├── local/
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│ ├── chain/
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│ │ ├── run_ivector_common.sh # Script for i-vector extraction during chain model training
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│ │ └── run_tdnn.sh # Script for training a TDNN model
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│ ├── data_prep.sh # Data preparation script for creating Kaldi data directories
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│ ├── download_and_untar.sh # Script for downloading and extracting datasets
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│ ├── download_lm.sh # Downloads language models
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│ ├── prepare_dict.sh # Prepares the pronunciation dictionary
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│ └── score.sh # Scoring script for evaluation
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├── path.sh # Script for setting Kaldi paths
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├── RESULTS # Script for printing the best WER results
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├── RESULTS.txt # Contains WER results from decoding
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├── run.sh # Main script for the entire training pipeline
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├── steps -> ../../wsj/s5/steps/ # Link to Kaldi’s WSJ steps for acoustic model training
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└── utils -> ../../wsj/s5/utils/ # Link to Kaldi’s utility scripts
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```
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### Key Files:
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- **cmd.sh**: Defines commands for running training and decoding tasks.
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- **path.sh**: Sets up paths for Kaldi binaries and scripts.
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- **run.sh**: Main entry point for the training pipeline, running tasks in stages.
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- **RESULTS**: Displays Word Error Rate (WER) for the trained models.
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## Installation
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### Prerequisites
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- [Kaldi](https://github.com/kaldi-asr/kaldi): Kaldi toolkit must be installed and configured.
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- Required tools: `ffmpeg`, `sox`, `sctk` for data preparation and scoring.
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### Steps
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1. Clone the Vosk API repository.
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2. Install Kaldi and ensure the `KALDI_ROOT` is correctly set in `path.sh`.
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3. Set environment variables using `cmd.sh` and `path.sh`.
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## Training Process
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### Data Preparation
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Run the data preparation stage in `run.sh`:
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```bash
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bash run.sh --stage 0 --stop_stage 0
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```
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This stage downloads and prepares the LibriSpeech dataset.
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### Dictionary Preparation
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Prepare the pronunciation dictionary with:
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```bash
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bash run.sh --stage 1 --stop_stage 1
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```
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This step generates the necessary files for Kaldi's `prepare_lang.sh` script.
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### MFCC Feature Extraction
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Run the MFCC extraction process:
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```bash
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bash run.sh --stage 2 --stop_stage 2
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```
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This step extracts Mel-frequency cepstral coefficients (MFCC) features and computes Cepstral Mean Variance Normalization (CMVN).
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### Acoustic Model Training
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Train monophone, LDA+MLLT, and SAT models:
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```bash
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bash run.sh --stage 3 --stop_stage 3
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```
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This stage trains GMM-based models and aligns the data for TDNN training.
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### TDNN Chain Model Training
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Train a Time-Delay Neural Network (TDNN) chain model:
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```bash
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bash run.sh --stage 4 --stop_stage 4
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```
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The chain model uses i-vectors for speaker adaptation.
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### Decoding
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After training, decode the test data:
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```bash
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bash run.sh --stage 5 --stop_stage 5
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```
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This step decodes using the trained model and evaluates the Word Error Rate (WER).
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## Results
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WER can be evaluated by running:
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```bash
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bash RESULTS
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```
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Example of `RESULTS.txt`:
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```plaintext
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%WER 14.10 [ 2839 / 20138, 214 ins, 487 del, 2138 sub ] exp/chain/tdnn/decode_test/wer_11_0.0
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%WER 12.67 [ 2552 / 20138, 215 ins, 406 del, 1931 sub ] exp/chain/tdnn/decode_test_rescore/wer_11_0.0
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``` |