Update README.md

README.md

@@ -4,197 +4,136 @@ datasets:
 - facebook/omnilingual-asr-corpus
 pipeline_tag: automatic-speech-recognition
 ---
-# Model Card for Model ID
 
-<!-- Provide a quick summary of what the model is/does. -->
+# Omnilingual ASR: Open-Source Multilingual Speech Recognition for 1600+ Languages
+
+<div align="center" style="lline-height: 1.2; font-size:16px; margin-bottom: 30px;">
+  <a href="https://huggingface.co/facebook" target="_blank" style="margin: 2px;">
+    🤗 Hugging Face 
+  </a> | 
+  <a href="https://github.com/facebookresearch/omnilingual-asr" target="_blank" style="margin: 2px;">
+    🐙 GitHub
+  </a> | 
+  <a href="https://huggingface.co/spaces/facebook/omniasr-transcriptions" target="_blank" style="margin: 2px;">
+    🤖️ Demo
+  </a> | 
+  <a href="https://ai.meta.com/research/publications/omnilingual-asr-open-source-multilingual-speech-recognition-for-1600-languages/" target="_blank" style="margin: 2px;">
+    📃 Paper
+  </a> | 
+  <a href="https://ai.meta.com/blog/omnilingual-asr-advancing-automatic-speech-recognition/" target="_blank" style="margin: 2px;">
+    📝 Blogpost
+  </a> | 
+  <a href="https://github.com/facebookresearch/omnilingual-asr/blob/main/LICENSE" style="margin: 2px;">
+    📄 License: Apache 2.0
+  </a>
+</div>
+
+# Model Card for omniASR-CTC-1B
+
+## Model Description
+
+This model is part of the **Omnilingual ASR** family released by Meta AI. The original suite includes:
+
+<!-- TODO : add new tokenizer, we'll get two tokenizer, add mssing speed numbers-->
+| Model Name          | Features      | Parameters | Download Size (FP32) | Inference VRAM¹ | Real-Time Factor¹ (relative speed)² |
+|---------------------|---------------|------------:|---------------:|---------------:|-----------:|
+| [`omniASR_W2V_300M`](https://huggingface.co/Steveeeeeeen/omniASR-W2V-300M)      | SSL  | 317_390_592   | 1.2 GiB | | |
+| [`omniASR_W2V_1B`](https://huggingface.co/Steveeeeeeen/omniASR-W2V-1B)          | SSL  | 965_514_752   | 3.6 GiB | | |
+| [`omniASR_W2V_3B`](https://huggingface.co/Steveeeeeeen/omniASR-W2V-3B)          | SSL  | 3_064_124_672 | 12.0 GiB | | |
+| [`omniASR_W2V_7B`](https://huggingface.co/Steveeeeeeen/omniASR-W2V-7B)          | SSL  | 6_488_487_168 | 25.0 GiB | | |
+| [`omniASR_CTC_300M`](https://huggingface.co/Steveeeeeeen/omniASR-CTC-300M)      | ASR  | 325_494_996   | 1.3 GiB   | ~2 GiB  | 0.001 (96x) |
+| [`omniASR_CTC_1B`](https://huggingface.co/Steveeeeeeen/omniASR-CTC-1B)          | ASR  | 975_065_300   | 3.7 GiB   | ~3 GiB  | 0.002 (48x) |
+| [`omniASR_CTC_3B`](https://huggingface.co/Steveeeeeeen/omniASR-CTC-3B)          | ASR  | 3_080_423_636 | 12.0 GiB  | ~8 GiB  | 0.003 (32x) |
+| [`omniASR_CTC_7B`](https://huggingface.co/Steveeeeeeen/omniASR-CTC-7B)          | ASR  | 6_504_786_132 | 25.0 GiB  | ~15 GiB | 0.006 (16x) |
+| [`omniASR_LLM_300M`](https://huggingface.co/Steveeeeeeen/omniASR-LLM-300M)      | ASR with optional language conditioning  | 1_627_603_584 | 6.1 GiB   | ~5 GiB  | 0.090 (~1x) |
+| [`omniASR_LLM_1B`](https://huggingface.co/Steveeeeeeen/omniASR-LLM-1B)          | ASR with optional language conditioning  | 2_275_710_592 | 8.5 GiB   | ~6 GiB  | 0.091 (~1x) |
+| [`omniASR_LLM_3B`](https://huggingface.co/Steveeeeeeen/omniASR-LLM-3B)          | ASR with optional language conditioning  | 4_376_679_040 | 17.0 GiB  | ~10 GiB | 0.093 (~1x) |
+| [`omniASR_LLM_7B`](https://huggingface.co/Steveeeeeeen/omniASR-LLM-7B)          | ASR with optional language conditioning  | 7_801_041_536 | 30.0 GiB  | ~17 GiB | 0.092 (~1x) |
+| [`omniASR_LLM_7B_ZS`](https://huggingface.co/Steveeeeeeen/omniASR-LLM-7B-ZS)    | Zero-Shot ASR | 7_810_900_608 | 30.0 GiB | ~20 GiB | 0.194 (~0.5x) |
+
+
+¹ (batch=1, audio_len=30s, BF16, A100)
+
+² Relative speed to `omniASR_LLM_7B`
 
-This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).
-
-## Model Details
-
-### Model Description
-
-<!-- Provide a longer summary of what this model is. -->
-
-
-
-- **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]
-
-### Model Sources [optional]
-
-<!-- Provide the basic links for the model. -->
-
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
-
-## Uses
-
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-
-### Direct Use
-
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-
-[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
+---
 
+## Installation
 
+The models were developed using [fairseq2](https://github.com/facebookresearch/fairseq2), a research-focused sequence modeling toolkit. While we provide a **reference** inference pipeline that works across platforms, audio support requires [libsndfile](https://github.com/facebookresearch/fairseq2?tab=readme-ov-file#system-dependencies) (Mac: `brew install libsndfile`; Windows may need an additional [setup](https://github.com/facebookresearch/fairseq2?tab=readme-ov-file#installing-on-windows)).
 
-## Model Examination [optional]
+```bash
+# using pip
+pip install omnilingual-asr
 
-<!-- Relevant interpretability work for the model goes here -->
+# using uv
+uv add omnilingual-asr
+```
 
-[More Information Needed]
 
-## Environmental Impact
+## Inference
 
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+```python
+from omnilingual_asr.models.inference.pipeline import ASRInferencePipeline
 
-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).
+pipeline = ASRInferencePipeline(model_card="omniASR_LLM_7B")
 
-- **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]
+audio_files = ["/path/to/eng_audio1.flac", "/path/to/deu_audio2.wav"]
+lang = ["eng_Latn", "deu_Latn"]
+transcriptions = pipeline.transcribe(audio_files, lang=lang, batch_size=2)
+```
 
-## Technical Specifications [optional]
+## Supported Languages
 
-### Model Architecture and Objective
+To view the full list of 1600+ supported languages, you can access the language list [programmatically](/src/omnilingual_asr/models/wav2vec2_llama/lang_ids.py):
 
-[More Information Needed]
+```python
+from omnilingual_asr.models.wav2vec2_llama.lang_ids import supported_langs
 
-### Compute Infrastructure
+# Print all supported languages
+print(f"Total supported languages: {len(supported_langs)}")
+print(supported_langs)
 
-[More Information Needed]
+# Check if a specific language is supported
+if "eng_Latn" in supported_langs:
+    print("English (Latin script) is supported!")
+```
 
-#### Hardware
+Languages follow the format `{language_code}_{script}`, for example `eng_Latn` - English (Latin script), `cmn_Hans` - Mandarin Chinese (Simplified), ...
 
-[More Information Needed]
+---
 
-#### Software
+## Training
 
-[More Information Needed]
+To further finetune the released checkpoints on your own data, use our [data preparation guide](/workflows/dataprep/README.md) followed by the [finetuning recipe guide](/workflows/recipes/wav2vec2/asr/README.md).
 
-## 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. -->
+## Citation
 
 **BibTeX:**
 
-[More Information Needed]
-
-**APA:**
-
-[More Information Needed]
-
-## Glossary [optional]
+```bibtex
+@misc{omnilingualasr2025,
+  title={{Omnilingual ASR}: Open-Source Multilingual Speech Recognition for 1600+ Languages},
+  author={{Omnilingual ASR Team} and Keren, Gil and Kozhevnikov, Artyom and Meng, Yen and Ropers, Christophe and Setzler, Matthew and Wang, Skyler and Adebara, Ife and Auli, Michael and Can, Balioglu and Chan, Kevin and Cheng, Chierh and Chuang, Joe and Droof, Caley and Duppenthaler, Mark and Duquenne, Paul-Ambroise and Erben, Alexander and Gao, Cynthia and Mejia Gonzalez, Gabriel and Lyu, Kehan and Miglani, Sagar and Pratap, Vineel and Sadagopan, Kaushik Ram and Saleem, Safiyyah and Turkatenko, Arina and Ventayol-Boada, Albert and Yong, Zheng-Xin and Chung, Yu-An and Maillard, Jean and Moritz, Rashel and Mourachko, Alexandre and Williamson, Mary and Yates, Shireen},
+  year={2025},
+  url={https://ai.meta.com/research/publications/omnilingual-asr-open-source-multilingual-speech-recognition-for-1600-languages/},
+}
+```
 
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+* **Developed by:** Meta AI / Omnilingual ASR Team([GitHub][1])
+* **Model type:** End-to-end automatic speech recognition model (wav2vec2-style encoder with CTC head / encoder-decoder, depending on checkpoint).
+* **Language(s) (NLP):** 1,600+ languages overall in Omnilingual ASR; this corpus release specifically covers **348 under-served languages** across many writing systems (Latin, Arabic, Devanagari, etc.).([GitHub][1])
+* **License:** Apache-2.0 (for the model and code), CC-BY-4.0 for the `facebook/omnilingual-asr-corpus` dataset.([GitHub][1])
 
-[More Information Needed]
-
-## More Information [optional]
-
-[More Information Needed]
-
-## Model Card Authors [optional]
+---
 
-[More Information Needed]
+[1]: https://github.com/facebookresearch/omnilingual-asr?tab=readme-ov-file "GitHub - facebookresearch/omnilingual-asr: Omnilingual ASR Open-Source Multilingual SpeechRecognition for 1600+ Languages"
+[2]: https://huggingface.co/datasets/facebook/omnilingual-asr-corpus/blob/main/README.md "README.md · facebook/omnilingual-asr-corpus at main"
+[3]: https://huggingface.co/datasets/facebook/omnilingual-asr-corpus?utm_source=chatgpt.com "facebook/omnilingual-asr-corpus · Datasets at ..."
+[4]: https://venturebeat.com/ai/meta-returns-to-open-source-ai-with-omnilingual-asr-models-that-can?utm_source=chatgpt.com "Meta returns to open source AI with Omnilingual ASR ..."
+[5]: https://huggingface.co/spaces/facebook/omniasr-transcriptions?utm_source=chatgpt.com "Omnilingual ASR Media Transcription"
+[6]: https://huggingface.co/collections/bezzam/omnilingual-asr-1-600-languages?utm_source=chatgpt.com "Omnilingual ASR (1600+ Languages) - a bezzam Collection"
 
-## Model Card Contact
 
-[More Information Needed]