speechmix 0.0.19

Explore different way to mix speech model(wav2vec2, hubert) and nlp model(BART,T5,GPT) together

SpeechMix

Explore different way to mix speech model(wav2vec2, hubert) and nlp model(BART,T5,GPT) together.

Introduction

For the same input:

from datasets import load_dataset
import soundfile as sf


# define function to read in sound file
def map_to_array(batch):
    speech, _ = sf.read(batch["file"])
    batch["speech"] = speech
    return batch


# load dummy dataset and read soundfiles
ds = load_dataset("patrickvonplaten/librispeech_asr_dummy", "clean", split="validation")
ds = ds.map(map_to_array)

transcript = ds['text'][0]
speech = ds["speech"][0]

Speech encoder NLP decoder

model = SpeechMixED("facebook/wav2vec2-base-960h", "facebook/bart-large")

transcript_tensor = model.tokenizer(transcript, return_tensors="pt").input_ids
speech_tensor = model.processor(speech, return_tensors="pt").input_values

model(speech_tensor, transcript_tensor)

Speech encoder NLP decoder only fine-tune on cross attention/projection/decoder embedding

model = SpeechMixED("facebook/wav2vec2-base-960h", "facebook/bart-large", ftl=True)

transcript_tensor = model.tokenizer(transcript, return_tensors="pt").input_ids
speech_tensor = model.processor(speech, return_tensors="pt").input_values

model(speech_tensor, transcript_tensor)

Speech encoder NLP encoder decoder

model = SpeechMixEED("facebook/wav2vec2-base-960h", "facebook/bart-large")

transcript_tensor = model.tokenizer(transcript, return_tensors="pt").input_ids
speech_tensor = model.processor(speech, return_tensors="pt").input_values

model(speech_tensor, transcript_tensor)

Speech encoder NLP encoder decoder only fine-tune on layer norm and attention

model = SpeechMixEED("facebook/wav2vec2-base-960h", "facebook/bart-large", lna=True)

transcript_tensor = model.tokenizer(transcript, return_tensors="pt").input_ids
speech_tensor = model.processor(speech, return_tensors="pt").input_values

model(speech_tensor, transcript_tensor)

Speech encoder NLP encoder decoder only fine-tune on speech encoder

model = SpeechMixEED("facebook/wav2vec2-base-960h", "facebook/bart-large", fne=True)

transcript_tensor = model.tokenizer(transcript, return_tensors="pt").input_ids
speech_tensor = model.processor(speech, return_tensors="pt").input_values

model(speech_tensor, transcript_tensor)

Installation

pip install

pip install speechmix

Build from source

git clone and cd into this project.

pip install -e .

Example

usage:
python train.py --speech_model_config facebook/wav2vec2-large-robust-ft-libri-960h --nlp_model_config facebook/mbart-large-50-one-to-many-mmt --SpeechMixEED --lna --dataset librispeech_asr --field clean --train_split train.100 --test_split validation --batch 3 --grad_accum 8

python train.py --speech_model_config facebook/wav2vec2-large-robust-ft-libri-960h --nlp_model_config facebook/mbart-large-50-one-to-many-mmt --SpeechMixEED --fne --dataset librispeech_asr --field clean --train_split train.100 --test_split validation --batch 3 --grad_accum 8

python train.py --speech_model_config facebook/wav2vec2-large-robust-ft-libri-960h --nlp_model_config facebook/mbart-large-50-one-to-many-mmt --SpeechMixED --dataset librispeech_asr --field other --train_split train.500 --test_split validation --batch 3 --grad_accum 8

python train.py --speech_model_config facebook/wav2vec2-large-robust-ft-libri-960h --nlp_model_config facebook/mbart-large-50-one-to-many-mmt --SpeechMixED --ftl --dataset librispeech_asr --field other --train_split train.500 --test_split validation --batch 3 --grad_accum 8

python train.py --speech_model_config facebook/wav2vec2-large-robust-ft-libri-960h --nlp_model_config facebook/mbart-large-50-one-to-many-mmt --SpeechMixSelf --dataset common_voice --field en --train_split train --test_split test --batch 3 --grad_accum 8

python train.py --speech_model_config facebook/wav2vec2-large-robust-ft-libri-960h --nlp_model_config facebook/mbart-large-50-one-to-many-mmt --SpeechMixEED --lna --dataset patrickvonplaten/librispeech_asr_dummy --field clean --train_split validation --test_split test --batch 3 --grad_accum 4