add readme

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+__pycache__
+.ipynb_checkpoints
+*.egg-info

README.md

@@ -3,197 +3,63 @@ library_name: transformers
 tags: []
 ---
 
-# Model Card for Model ID
+# EMOVA Speech Tokenizer HF
 
-<!-- Provide a quick summary of what the model is/does. -->
+## Model Summary
 
+This repo contains the discrete speech tokenizer used to train the [EMOVA](https://emova-ollm.github.io/) series of models.  With a semantic-acoustic disentangled design, it not only facilitates seamless omni-modal alignment among vision, language and audio modalities, but also empowers flexible speech style controls including emotions and pitches. It contains a **speech-to-unit (S2U)** tokenizer to convert speech signals to discrete speech units, and a **unit-to-speech (U2S)** de-tokenizer to reconstruct speech signals from the speech units.
 
+This repo wraps the original [EMOVA speech tokenizer](https://huggingface.co/Emova-ollm/emova_speech_tokenizer) with HuggingFace [PreTrainedModel](https://huggingface.co/docs/transformers/v4.47.1/main_classes/model#transformers.PreTrainedModel) for simpler usage.
 
-## Model Details
+## Install
 
-### Model Description
+```bash
+git clone https://huggingface.co/Emova-ollm/emova_speech_tokenizer
+cd emova_speech_tokenizer
 
-<!-- Provide a longer summary of what this model is. -->
+# for GPU
+pip install -e .
 
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
+# for NPU
+# check https://github.com/Ascend/pytorch?tab=readme-ov-file#installation for detailed installation of torch npu
+pip install -e .[npu]
+```
 
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
+## Usage
 
-### Model Sources [optional]
+```diff
+import torch
++import torch_npu # add it if you want to use NPU
++from torch_npu.contrib import transfer_to_npu # add it if you want to use NPU
 
-<!-- Provide the basic links for the model. -->
+from transformers import AutoModel
 
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+# load pretrained model
+model = AutoModel.from_pretrained("Emova-ollm/emova_speech_tokenizer", torch_dtype=torch.float32, trust_remote_code=True).eval().cuda()
 
-## Uses
+# S2U
+wav_file = "./examples/s2u/example.wav"
+speech_unit = model.encode(wav_file)
+print(speech_unit)
 
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+# U2S
+emotion = random.choice(['angry', 'happy', 'neutral', 'sad'])
+speed = random.choice(['normal', 'fast', 'slow'])
+pitch = random.choice(['normal', 'high', 'low'])
+gender = random.choice(['female', 'male'])
+condition = f'gender-{gender}_emotion-{emotion}_speed-{speed}_pitch-{pitch}'
 
-### Direct Use
+output_wav_file = f'./examples/u2s/{condition}_output.wav'
+model.decode(speech_unit, condition=condition, output_wav_file=output_wav_file)
+```
 
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+## Citation
 
-[More Information Needed]
-
-### Downstream Use [optional]
-
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-
-[More Information Needed]
-
-### Out-of-Scope Use
-
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
-
-## Bias, Risks, and Limitations
-
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
-
-### Recommendations
-
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-
-## How to Get Started with the Model
-
-Use the code below to get started with the model.
-
-[More Information Needed]
-
-## Training Details
-
-### Training Data
-
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-
-[More Information Needed]
-
-### Training Procedure
-
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-
-#### Preprocessing [optional]
-
-[More Information Needed]
-
-
-#### Training Hyperparameters
-
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-
-#### Speeds, Sizes, Times [optional]
-
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-
-[More Information Needed]
-
-## Evaluation
-
-<!-- This section describes the evaluation protocols and provides the results. -->
-
-### Testing Data, Factors & Metrics
-
-#### Testing Data
-
-<!-- This should link to a Dataset Card if possible. -->
-
-[More Information Needed]
-
-#### Factors
-
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
-[More Information Needed]
-
-#### Metrics
-
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
-
-### Results
-
-[More Information Needed]
-
-#### Summary
-
-
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
-
-## Environmental Impact
-
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-
-## Technical Specifications [optional]
-
-### Model Architecture and Objective
-
-[More Information Needed]
-
-### Compute Infrastructure
-
-[More Information Needed]
-
-#### Hardware
-
-[More Information Needed]
-
-#### Software
-
-[More Information Needed]
-
-## Citation [optional]
-
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-
-**BibTeX:**
-
-[More Information Needed]
-
-**APA:**
-
-[More Information Needed]
-
-## Glossary [optional]
-
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-
-[More Information Needed]
-
-## More Information [optional]
-
-[More Information Needed]
-
-## Model Card Authors [optional]
-
-[More Information Needed]
-
-## Model Card Contact
-
-[More Information Needed]
+```bibtex
+@article{chen2024emova,
+  title={Emova: Empowering language models to see, hear and speak with vivid emotions},
+  author={Chen, Kai and Gou, Yunhao and Huang, Runhui and Liu, Zhili and Tan, Daxin and Xu, Jing and Wang, Chunwei and Zhu, Yi and Zeng, Yihan and Yang, Kuo and others},
+  journal={arXiv preprint arXiv:2409.18042},
+  year={2024}
+}
+```