DHdroid/nlp-improving-asr

A project for 2023-2 SNU NLP lecture (Team 5)

A project for 2023-Fall SNU NLP lecture (Team 5)

This codebase is built on Whisper and WhisperBiasing

Method Overview

Shallow Fusion

Utillize the information from the LM outputs as follows: (AM for Audio Model, LM for Language Model)

$$\text{score} = log P_{\text{AM}}(Y|X) + \lambda{}\cdot{}logP_{\text{LM}}(Y)$$

In this project, we used Whisper-base.en for AM, and GPT-2-small for LM.

Few-shot Prompted Shallow Fusion (Proposed Method 1)

Give LM few-shot examples to 1) provide LM with the following context and 2) leverage in-context learning ability of LM.

$$\text{score} = log P_{\text{AM}}(Y|X) + \lambda{}\cdot{}logP_{\text{LM}}(Y | \text{few-shot prompt})$$

Combined Shallow Fusion (Proposed Method 2)

• Generate first $K$ tokens with Few-shot Prompted Shallow Fusion
• Generate the remaining tokens with naive shallow fusion

$$ \text{score} = \begin{cases} log P_{\text{AM}}(Y|X) + \lambda{}\cdot{}logP_{\text{LM}}(Y | \text{few-shot prompt}), & \text{if $\text{len}(Y) \leq{} K$,} \\ log P_{\text{AM}}(Y|X) + \lambda{}\cdot{}logP_{\text{LM}}(Y), & \text{otherwise,} \end{cases} $$

Experimental Results on LibriSpeech

Used beam_size=5, $\lambda{}$=0.05 in all experiments. Random retrieval is used for few-shot prompting.

Methods	test-clean (WER)	test-other (WER)	Average
Whisper	4.35	9.42	6.89
Shallow Fusion	3.97	9.46	6.72
Few-shot Prompted Shallow Fusion	4.26	9.42	6.84
Combined Shallow Fusion	4.0	9.23	6.62

How to run our codes

Install Dependencies

pip install -r requirements.txt

Example

1. Generate data pool

Example command :

python3 decode_librispeech.py \
    --batch_size 1 \
    --beam_size 5 \
    --split $YOUR_SPLIT \
    --output_path $YOUR_OUTPUT_PATH \
    --cache_root $YOUR_CACHE_ROOT \

2. Filter the data pool(Optional, Recommended)

Use filter_csv.sh. In this file, replace input csv path, output csv path to yours and select filtering parameter min_wer, max_wer.

3. Generate vector DB(Optional)

If you want to retrieve examples using similarity search, you have to build your faiss index by calling search_sentence.py.

4. Inference

Example command :

python3 decode_librispeech.py \
    --batch_size 1 \
    --beam_size 5 \
    --use_gpt \
    --gpt_kind gpt2 \
    --shallow_fusion \
    --use_icl \
    --sample_random \
    --split $YOUR_SPLIT \
    --output_path $YOUR_OUTPUT_PATH \
    --cache_root $YOUR_CACHE_ROOT \

Arguments

--whisper_model : Kind of whisper model. Default is "base.en"
--split : The dataset type. Default is "test-clean"
--use_gpt2 : Select whether to use lm or not. Default is "False"
--gpt_kind : The model of lm. Default is "gpt2"
--lm_weight : The weight of shallow fusion. Default is "0.05"
--ilm_weight : The weight of internal lm weight. In our project this argument should be 0. Default is "0"
--shallow_fusion : Select whether to use shallow fusion. Default is "False"
--batch_size : Batch size. Default is "1"
--beam_size : Depth of beam search.. Default is "5"
--cache_root : Cache_root.
--num_data : Number of datas to decode. When this argument is "-1", decode all datas. Default is "-1"
--dataset_offset : Dataset offset. Default is "0"
--use_icl : Select whether to use few-shot prompting. Default is "False"
--index_path : Path of index file.
--csv_path : Path of csv file.
--output_path : Path of output file.
--num_examples : Number of prompt examples. Default is "10"
--sample_random : Select whether to create prompt randomly or not. Default is "False"
--prefix_lenght : Lenght of prefix tokens. Default is "3"