Hugging Face's logo

fnlp
/
SpeechGPT-7B-cm

Text Generation
Transformers
PyTorch
llama
text-generation-inference
Inference Endpoints
Model card Files Community
1
Edit model card
YAML Metadata Warning: empty or missing yaml metadata in repo card (https://huggingface.co/docs/hub/model-cards#model-card-metadata)

SpeechGPT: Empowering Large Language Models with Intrinsic Cross-Modal Conversational Abilities


Introduction

SpeechGPT is a large language model with intrinsic cross-modal conversational abilities, capable of perceiving and generating multi-model content following human instructions. With discrete speech representations, we first construct SpeechInstruct, a large-scale cross-modal speech instruction dataset. Additionally, we employ a three-stage training strategy that includes modality-adaptation pre-training, cross-modal instruction fine-tuning, and chain-of-modality instruction fine-tuning. The experimental results demonstrate that SpeechGPT has an impressive capacity to follow multi-modal human instructions and highlight the potential of handling multiple modalities with one model.
SpeechGPT demos are shown in our project page. As shown in the demos, SpeechGPT has strong cross-modal instruction-following ability and spoken dialogue ability. SpeechGPT can be a talking encyclopedia, your personal assistant, your chat partner, a poet, a psychologist and your educational assistant...




SpeechGPT’s capabilities to tackle multiple cross-modal tasks




Left: SpeechInstruct construction process. Right: SpeechGPT model structure

Release

  • [2023/9/15] We released SpeechGPT code and checkpoints and SpeechInstruct dataset.
  • [2023/9/1] We proposed SpeechTokenizer: Unified Speech Tokenizer for Speech Language Models. We released the code and checkpoints of SpeechTokenizer. Checkout the paper, demo and github.
  • [2023/5/18] We released SpeechGPT: Empowering Large Language Models with Intrinsic Cross-Modal Conversational Abilities. We propose SpeechGPT, the first multi-modal LLM capable of perceiving and generating multi-modal contents following multi-modal human instructions. Checkout the paper and demo.

Table of Contents

Open-source list

Models

  • SpeechGPT-7B-ma: The model obtained after the first-stage modality-adaptation pre-training, which was initialized with LLaMA-7B and further pre-trained on LibriLight speech units.
  • SpeechGPT-7B-cm: The model obtained after the second-stage cross-modal instruction finetuning, which was initialized with SpeechGPT-7B-ma and further finetuned on SpeechInstruct Cross-Modal Instruction set. This is a powerful foundational model that aligns speech and text.
  • SpeechGPT-7B-com: The model obtained after the third-stage chain-of-modality instruction finetuning, which was initialized with SpeechGPT-7B-cm and further lora-finetuned on SpeechInstruct Chain-of-Modality Instruction set. This is an adapter-model of SpeechGPT-7B-cm for spoken dialogue.

Datasets

  • SpeechInstruct-cross-modal: The cross-modal instruction set, about 9 million unit-text data pairs tokenzed by mHuBERT from large-scale English ASR datasets. data format:
  • SpeechInstruct-chain-of-modality: The chain-of-thought style instructions for four input-output formats, namely Speech Instruction-Speech Response, Speech Instruction-Text Response, Text Instruction-Speech Response, and Text Instruction-Text Response.

SpeechInstruct-cross-modal data format:

[
    {
        "prefix": "You are an AI assistant whose name is SpeechGPT.\n- SpeechGPT is a intrinsic cross-modal conversational language model that is developed by Fudan University.  SpeechGPT can understand and communicate fluently with human through speech or text chosen by the user.\n- It can perceive cross-modal inputs and generate cross-modal outputs.\n",
        "plain_text": "[Human]: Try to speak out this sentence, please. This is input: The alchemist rode in front, with the falcon on his shoulder.<eoh> [SpeechGPT]: <sosp><661><588><604><157><596><499><596><106><596><189><63><189><665><991><162><202><393><946><327><905><907><597><660><351><557><794><788><59><754><12><977><877><333><873><835><67><940><118><686><613><169><72><644><553><535><935><101><741><384><173><894><787><380><787><196><555><721><944><250><56><812><222><915><143><390><479><330><435><647><246><650><816><325><506><686><208><613><417><755><193><411><452><111><735><6><735><63><665><644><991><535><271><333><196><918><29><202><393><946><734><390><479><330><776><167><761><907><597><660><351><557><794><75><788><15><366><896><627><168><654><659><177><183><609><710><187><493><361><470><821><59><56><198><912><742><840><431><531><76><668><576><803><791><380><660><325><801><549><366><377><164><309><584><605><193><71><39><eosp><eoa> "
    },
]

SpeechInstruct-chain-of-modality data format:

[
    {
        "prefix": "You are an AI assistant whose name is SpeechGPT.\n- SpeechGPT is a intrinsic cross-modal conversational language model that is developed by Fudan University.  SpeechGPT can understand and communicate fluently with human through speech or text chosen by the user.\n- It can perceive cross-modal inputs and generate cross-modal outputs.\n",
        "plain_text": "[Human]: <sosp><661><987><511><732><951><997><111><982><189><63><665><991><535><101><741><173><945><944><503><641><124><565><734><870><290><978><833><238><761><907><430><901><185><403><557><244><583><788><663><969><896><627><143><515><663><969><660><691><251><412><260><41><740><677><253><380><382><268><506><876><417><755><16><819><80><651><80><651><80><987><588><eosp><eoh>. [SpeechGPT]: What is a bad term for poop?; [ta] A bad term for poop is excrement. It is usually used as a polite way to refer to fecal waste.; [ua] <sosp><497><63><264><644><710><823><565><577><154><331><384><173><945><29><244><326><583><728><576><663><969><896><627><143><38><515><663><24><382><251><676><412><260><41><740><677><253><382><268><876><233><878><609><389><771><865><641><124><878><609><423><384><879><487><219><522><589><337><126><119><663><748><12><671><877><377><385><902><819><619><842><419><997><829><111><666><42><277><63><665><644><389><771><685><437><641><124><258><436><139><340><11><59><518><56><948><86><258><436><139><340><347><376><940><118><944><878><173><641><124><362><734><179><961><931><878><609><423><384><879><219><522><866><337><243><935><101><741><822><89><194><630><86><555><105><79><868><220><156><824><998><870><390><422><330><776><663><969><523><105><79><799><220><357><390><479><422><330><776><485><165><86><501><119><716><205><521><787><935><101><741><89><194><664><835><67><940><118><613><417><755><902><415><772><497><eosp><eoa>."
    },
]

Talk with SpeechGPT

Due to limited training data and resources, the performance of the open-source SpeechGPT is currently not optimal. Problems such as task recognition errors and inaccuracies in speech recognition may occur. As this project is primarily an exploration in research, we have not increased the amount of pretraining and sft data or training steps to enhance performance. Our hope is that SpeechGPT can serve as a foundational model to encourage research and exploration in the field of speech language models.

Installation 

git clone https://github.com/0nutation/SpeechGPT
cd SpeechGPT
conda create --name SpeechGPT python=3.8
conda activate SpeechGPT
pip install -r requirements.txt

Download

To talk with SpeechGPT, you should download SpeechGPT-7B-cm and SpeechGPT-7B-com locally.

You should download mHuBERT model to utils/speech2unit/. Please see Speech2unit for details.

s2u_dir="uitls/speech2unit"
cd ${s2u_dir}
wget https://dl.fbaipublicfiles.com/hubert/mhubert_base_vp_en_es_fr_it3.pt
wget https://dl.fbaipublicfiles.com/hubert/mhubert_base_vp_en_es_fr_it3_L11_km1000.bin

You should download the unit-vocoder to utils/vocoder/. Please see vocoder for details.

vocoder_dir="utils/vocoder/"
cd ${vocoder_dir}
wget https://dl.fbaipublicfiles.com/fairseq/speech_to_speech/vocoder/code_hifigan/mhubert_vp_en_es_fr_it3_400k_layer11_km1000_lj/config.json -O config.json
wget https://dl.fbaipublicfiles.com/fairseq/speech_to_speech/vocoder/code_hifigan/mhubert_vp_en_es_fr_it3_400k_layer11_km1000_lj/g_00500000 -O vocoder.pt

CLI Inference 

python3 speechgpt/src/infer/cli_infer.py \
--model-name-or-path "path/to/SpeechGPT-7B-cm" \
--lora-weights "path/to/SpeechGPT-7B-com" \
--s2u-dir "${s2u_dir}" \
--vocoder-dir "${vocoder_dir} \
--output-dir "output"

Notes For speech input, you can provide the path to the audio file. For ASR or TTS tasks, you must prefix the speech or text with this is input: , otherwise, it may be recognized incorrectly.

The speech response will be saved to a .wav file, and detailed responses will be saved in a JSON file. The paths to these files will be indicated in the response.

Here are some examples of talking with SpeechGPT:

Textual dialogue example

Please talk with SpeechGPT:
Who is Lebron James?
Response:
   Lebron James is an American professional basketball player for the Los Angeles Lakers of the National Basketball Association (NBA). He is considered one of the greatest basketball players of all time and is known for his athleticism, scoring ability, and leadership skills. He is a four-time NBA MVP, a 14-time NBA All-Star, a 13-time All-NBA selection, and a two-time Olympic gold medalist.
Response json is saved in output/responses.json

Spoken dialogue example

Please talk with SpeechGPT:
prompts/0.wav
Transcript:   What are the main causes of climate change?
Text response:  The main causes of climate change are human activities such as burning fossil fuels, deforestation, and agricultural practices. These activities release greenhouse gases, like carbon dioxide and Methane, into the atmosphere which trap heat and cause the Earth's temperature to rise.
Speech repsonse is saved in output/wav/answer_0.wav
Response json is saved in output/responses.json

ASR example

Please talk with SpeechGPT:
Recognize this speech, this is input: prompts/1.wav
Response:
   today is a sunny day.
Response json is saved in output/responses.json

TTS example

Please talk with SpeechGPT:
Read this sentence aloud, this is input: Today is a sunny day.
Response:
   <sosp> <661> <987> <520> <982> <681> <982> <681> <982> <681> <982> <681> <982> <189> <63> <662> <79> <868> <220> <196> <166> <549> <822> <89> <194> <633> <14> <855> <183> <609> <389> <771> <865> <641> <124> <362> <734> <742> <98> <519> <26> <204> <280> <668> <167> <104> <650> <179> <961> <428> <950> <82> <165> <196> <166> <549> <822> <89> <194> <458> <726> <603> <819> <651> <133> <651> <133> <186> <133> <186> <133> <186> <511> <186> <511> <eosp> 
Speech repsonse is saved in output/wav/answer_1.wav
Response json is saved in output/responses.json

Gradio Web UI 

python3 speechgpt/src/infer/web_infer.py \
--model-name-or-path "path/to/SpeechGPT-7B-cm" \
--lora-weights "path/to/SpeechGPT-7B-com" \
--s2u-dir "${s2u_dir}" \
--vocoder-dir "${vocoder_dir}" \
--output-dir "output/"

Train SpeechGPT

Stage1: Modality-adaptation Pre-training

First, utilize mHuBERT for discretizing the LibriLight dataset to obtain discrete unit sequences for stage1 training. You can refer to the data processing methods in Speech2unit.

Second, divide the discrete units into a training set and a development set, and save them in the following format in the files data/stage1/train.txt and data/stage1/dev.txt:

<sosp><189><247><922><991><821><258><485><974><284><466><969><523><196><202><881><331><822><853><432><32><742><98><519><26><204><280><576><384><879><901><555><944><366><641><124><362><734><156><824><462><761><907><430><81><597><716><205><521><470><821><677><355><483><641><124><243><290><978><82><620><915><470><821><576><384><466><398><212><455><931><579><969><778><45><914><445><469><576><803><6><803><791><377><506><835><67><940><613><417><755><237><224><452><121><736><eosp>
<sosp><300><189><63><6><665><991><881><331><6><384><879><945><29><244><583><874><655><837><81><627><545><124><337><850><412><213><260><41><740><797><211><488><961><428><6><196><555><944><873><32><683><700><955><812><328><915><166><250><56><903><86><233><479><330><776><167><104><764><259><921><366><663><432><431><531><976><314><822><89><664><377><611><479><417><eosp>
<sosp><189><735><991><39><565><734><32><742><98><519><26><204><280><668><576><803><791><660><555><233><787><101><741><466><969><219><107><459><491><556><384><733><219><501><445><137><910><523><793><50><981><230><534><321><948><86><116><281><62><462><104><70><918><743><15><212><455><143><836><173><944><958><390><422><66><776><258><436><139><663><432><742><98><519><589><243><126><260><41><444><6><655><764><969><219><727><85><297><700><362><493><6><493><361><393><946><6><470><821><246><655><837><81><969><916><584><819><544><452><158><452><736><eosp>

Third, you should download LLaMA 7B(HuggingFace) to llama/hf/7B.

Now you can start stage1 training: To perform distributed training, you must specify the correct values for NNODE, NODE_RANK, MASTER_ADDR, and MASTER_PORT.

bash scripts/ma_pretrain.sh ${NNODE} ${NODE_RANK} ${MASTER_ADDR} ${MASTER_PORT}

Stage 2: Cross-modal Instruction Finetuning

You should download SpeechInstruct Cross-modal Instruction set to data/stage2/.

If you want to skip stage1 training, you can download SpeechGPT-7B-ma to output/stage1/.

Now you can start stage2 training: To perform distributed training, you must specify the correct values for NNODE, NODE_RANK, MASTER_ADDR, and MASTER_PORT.

bash scripts/cm_sft.sh ${NNODE} ${NODE_RANK} ${MASTER_ADDR} ${MASTER_PORT}

Stage 3: Chain-of-modality Instruction Finetuning

You should download SpeechInstruct Chain-of-modality Instruction set to data/stage3/.

If you want to skip stage1 and stage2, you can download SpeechGPT-7B-cm to output/stage2/.

Now you can start stage3 training: To perform distributed training, you must specify the correct values for NNODE, NODE_RANK, MASTER_ADDR, and MASTER_PORT.

bash scripts/com_sft.sh ${NNODE} ${NODE_RANK} ${MASTER_ADDR} ${MASTER_PORT}

Finetune SpeechGPT

Speech-7B-cm is a foundational model with strong alignment between speech and text. We encourage fine-tuning SpeechGPT based on this model.

Step1: prepare your data following the format in SpeechInstruct Cross-modal Instruction set.

Step2: download SpeechGPT-7B-cm locally.

Step3: Modify the METAROOT, DATAROOT, and OUTROOT parameters in the scripts/cm_sft.sh script to yours and then run it. For LoRA fine-tuning, update the METAROOT, DATAROOT, and OUTROOT parameters in the scripts/com_sft.sh script and run it.

Acknowledgements

Citation

If you find SpeechGPT useful for your research and applications, please cite using the BibTex:

@misc{zhang2023speechgpt,
      title={SpeechGPT: Empowering Large Language Models with Intrinsic Cross-Modal Conversational Abilities}, 
      author={Dong Zhang and Shimin Li and Xin Zhang and Jun Zhan and Pengyu Wang and Yaqian Zhou and Xipeng Qiu},
      year={2023},
      eprint={2305.11000},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}
Downloads last month
1,830
Inference Examples
Text Generation
This model does not have enough activity to be deployed to Inference API (serverless) yet. Increase its social visibility and check back later, or deploy to Inference Endpoints (dedicated) instead.