Update README.md

README.md

@@ -9,7 +9,7 @@ pipeline_tag: text-to-speech
 library_name: transformers
 ---
 
-# Higgs Audio V2: Redefining Expressiveness in Audio Generation
+# Higgs TTS 2: Redefining Expressiveness in Audio Generation
 
 <div align="center" style="display: flex; justify-content: center; margin-top: 10px; flex-wrap: wrap; gap: 8px;">
   <a href="https://boson.ai/blog/higgs-audio-v2"><img src='https://img.shields.io/badge/🚀-Launch Blogpost-228B22' style="margin-right: 5px;"></a>
@@ -20,8 +20,10 @@ library_name: transformers
 
 Check our open-source repository https://github.com/boson-ai/higgs-audio for more details!
 
-We are open-sourcing Higgs Audio v2, a powerful audio foundation model pretrained on over 10 million hours of audio data and a diverse set of text data.
-Despite having no post-training or fine-tuning, Higgs Audio v2 excels in expressive audio generation, thanks to its deep language and acoustic understanding.
+> **Rename note:** Higgs Audio V2 and Higgs Audio V2 Generation have been renamed to Higgs TTS 2.
+
+We are open-sourcing Higgs TTS 2, a powerful audio foundation model pretrained on over 10 million hours of audio data and a diverse set of text data.
+Despite having no post-training or fine-tuning, Higgs TTS 2 excels in expressive audio generation, thanks to its deep language and acoustic understanding.
 
 On [EmergentTTS-Eval](https://github.com/boson-ai/emergenttts-eval-public), the model achieves win rates of **75.7%** and **55.7%** over "gpt-4o-mini-tts" on the "Emotions" and "Questions" categories, respectively. It also obtains state-of-the-art performance on traditional TTS benchmarks like Seed-TTS Eval and Emotional Speech Dataset (ESD). Moreover, the model demonstrates capabilities rarely seen in previous systems, including automatic prosody adaptation during narration, zero-shot generation of natural multi-speaker dialogues in multiple languages, melodic humming with the cloned voice, and simultaneous generation of speech and background music.
 
@@ -54,7 +56,7 @@ Here's another demo video that show-cases the model's multilingual capability an
     <img src="./higgs_audio_v2_architecture_combined.png" width=900>
 </p>
 
-Higgs Audio v2 adopts the "generation variant" depicted in the architecture figure above. Its strong performance is driven by three key technical innovations:
+Higgs TTS 2 adopts the "generation variant" depicted in the architecture figure above. Its strong performance is driven by three key technical innovations:
 
 - We developed an automated annotation pipeline that leverages multiple ASR models, sound event classification models, and our in-house audio understanding model. Using this pipeline, we cleaned and annotated 10 million hours audio data, which we refer to as AudioVerse. The in-house understanding model is finetuned on top of Higgs Audio v1 Understanding, which adopts the "understanding variant" shown in the architecture figure.
 - We trained a unified audio tokenizer from scratch that captures both semantic and acoustic features.
@@ -74,22 +76,22 @@ Check https://huggingface.co/bosonai/higgs-audio-v2-tokenizer for more informati
 
 ### Model Architecture -- Dual FFN
 
-Higgs Audio v2 is built on top of [Llama-3.2-3B](https://huggingface.co/meta-llama/Llama-3.2-3B). To enhance the model’s ability to process audio tokens,
+Higgs TTS 2 is built on top of [Llama-3.2-3B](https://huggingface.co/meta-llama/Llama-3.2-3B). To enhance the model’s ability to process audio tokens,
 we incorporate the "DualFFN" architecture as an audio adapter.
 DualFFN acts as an audio-specific expert, boosting the LLM's performance with minimal computational overhead.
 Our implementation preserves 91% of the original LLM’s training speed with the inclusion of DualFFN, which has 2.2B parameters.
-Thus, the total number of parameter for Higgs Audio v2 is 3.6B (LLM) + 2.2B (Audio Dual FFN), and it has the same training / inference FLOPs as Llama-3.2-3B.
+Thus, the total number of parameter for Higgs TTS 2 is 3.6B (LLM) + 2.2B (Audio Dual FFN), and it has the same training / inference FLOPs as Llama-3.2-3B.
 Ablation study shows that the model equipped with DualFFN consistently outperforms its counterpart in terms of word error rate (WER) and speaker similarity.
 See [our architecture blog](https://github.com/boson-ai/higgs-audio/blob/main/tech_blogs/ARCHITECTURE_BLOG.md) for more information.
 
 
 ## Evaluation
 
-Here's the performance of Higgs Audio v2 on four benchmarks,  [Seed-TTS Eval](https://github.com/BytedanceSpeech/seed-tts-eval), [Emotional Speech Dataset (ESD)](https://paperswithcode.com/dataset/esd), [EmergentTTS-Eval](https://arxiv.org/abs/2505.23009), and Multi-speaker Eval:
+Here's the performance of Higgs TTS 2 on four benchmarks,  [Seed-TTS Eval](https://github.com/BytedanceSpeech/seed-tts-eval), [Emotional Speech Dataset (ESD)](https://paperswithcode.com/dataset/esd), [EmergentTTS-Eval](https://arxiv.org/abs/2505.23009), and Multi-speaker Eval:
 
 #### Seed-TTS Eval & ESD
 
-We prompt Higgs Audio v2 with the reference text, reference audio, and target text for zero-shot TTS. We use the standard evaluation metrics from Seed-TTS Eval and ESD.
+We prompt Higgs TTS 2 with the reference text, reference audio, and target text for zero-shot TTS. We use the standard evaluation metrics from Seed-TTS Eval and ESD.
 
 |                              | SeedTTS-Eval| | ESD   |                 |
 |------------------------------|--------|--------|---------|-------------------|
@@ -98,16 +100,16 @@ We prompt Higgs Audio v2 with the reference text, reference audio, and target te
 | Qwen2.5-omni†                | 2.33   | 64.10  | -       | -                 |
 | ElevenLabs Multilingual V2   | **1.43**   | 50.00  | 1.66    | 65.87             |
 | Higgs Audio v1                | 2.18   | 66.27  | **1.49**    | 82.84             |
-| Higgs Audio v2 (base)         | 2.44   | **67.70**  | 1.78    | **86.13**         |
+| Higgs TTS 2 (base)            | 2.44   | **67.70**  | 1.78    | **86.13**         |
 
 
 #### EmergentTTS-Eval ("Emotions" and "Questions")
 
-Following the [EmergentTTS-Eval Paper](https://arxiv.org/abs/2505.23009), we report the win-rate over "gpt-4o-mini-tts" with the "alloy" voice. Results of Higgs Audio v2 is obtained with the voice of "belinda". The judge model is Gemini 2.5 Pro.
+Following the [EmergentTTS-Eval Paper](https://arxiv.org/abs/2505.23009), we report the win-rate over "gpt-4o-mini-tts" with the "alloy" voice. Results of Higgs TTS 2 are obtained with the voice of "belinda". The judge model is Gemini 2.5 Pro.
 
 | Model                              | Emotions (%) ↑ | Questions (%) ↑ |
 |------------------------------------|--------------|----------------|
-| Higgs Audio v2 (base)               | **75.71%**   | **55.71%**         |
+| Higgs TTS 2 (base)                  | **75.71%**   | **55.71%**         |
 | [gpt-4o-audio-preview†](https://platform.openai.com/docs/models/gpt-4o-audio-preview)       | 61.64%       | 47.85%         |
 | [Hume.AI](https://www.hume.ai/research)                            | 61.60%       | 43.21%         |
 | **BASELINE:** [gpt-4o-mini-tts](https://platform.openai.com/docs/models/gpt-4o-mini-tts)  | 50.00%       | 50.00%         |
@@ -122,14 +124,14 @@ Following the [EmergentTTS-Eval Paper](https://arxiv.org/abs/2505.23009), we rep
 
 #### Multi-speaker Eval
 
-We also designed a multi-speaker evaluation benchmark to evaluate the capability of Higgs Audio v2 for multi-speaker dialog generation. The benchmark contains three subsets
+We also designed a multi-speaker evaluation benchmark to evaluate the capability of Higgs TTS 2 for multi-speaker dialog generation. The benchmark contains three subsets
 
 - `two-speaker-conversation`: 1000 synthetic dialogues involving two speakers. We fix two reference audio clips to evaluate the model's ability in double voice cloning for utterances ranging from 4 to 10 dialogues between two randomly chosen persona.
 - `small talk (no ref)`: 250 synthetic dialogues curated in the same way as above, but are characterized by short utterances and a limited number of turns (4–6), we do not fix reference audios in this case and this set is designed to evaluate the model's ability to automatically assign appropriate voices to speakers.
 - `small talk (ref)`: 250 synthetic dialogues similar to above, but contains even shorter utterances as this set is meant to include reference clips in it's context, similar to `two-speaker-conversation`.
 
 
-We report the word-error-rate (WER) and the geometric mean between intra-speaker similarity and inter-speaker dis-similarity on these three subsets. Other than Higgs Audio v2, we also evaluated [MoonCast](https://github.com/jzq2000/MoonCast) and [nari-labs/Dia-1.6B-0626](https://huggingface.co/nari-labs/Dia-1.6B-0626), two of the most popular open-source models capable of multi-speaker dialog generation.
+We report the word-error-rate (WER) and the geometric mean between intra-speaker similarity and inter-speaker dis-similarity on these three subsets. Other than Higgs TTS 2, we also evaluated [MoonCast](https://github.com/jzq2000/MoonCast) and [nari-labs/Dia-1.6B-0626](https://huggingface.co/nari-labs/Dia-1.6B-0626), two of the most popular open-source models capable of multi-speaker dialog generation.
 Results are summarized in the following table. We are not able to run [nari-labs/Dia-1.6B-0626](https://huggingface.co/nari-labs/Dia-1.6B-0626) on our "two-speaker-conversation" subset due to its strict limitation on the length of the utterances and output audio.
 
 |                                                | two-speaker-conversation |                |small talk |                | small talk (no ref) |                |
@@ -137,14 +139,14 @@ Results are summarized in the following table. We are not able to run [nari-labs
 |                                                | WER ↓                      | Mean Sim & Dis-sim ↑ | WER ↓       |  Mean Sim & Dis-sim ↑ | WER ↓               | Mean Sim & Dis-sim ↑ |
 | [MoonCast](https://github.com/jzq2000/MoonCast) | 38.77                    | 46.02         | **8.33**       | 63.68          | 24.65               | 53.94 |
 | [nari-labs/Dia-1.6B-0626](https://huggingface.co/nari-labs/Dia-1.6B-0626)         | \-                       | \-             | 17.62      | 63.15          | 19.46               | **61.14**          |
-| Higgs Audio v2 (base)     | **18.88**                    | **51.95**          | 11.89      | **67.92**              | **14.65**               | 55.28              |
+| Higgs TTS 2 (base)        | **18.88**                    | **51.95**          | 11.89      | **67.92**              | **14.65**               | 55.28              |
 
 
 ## Usage
 
 ### Transformers 🤗
 
-Higgs Audio V2 is supported natively in `transformers`: [see the doc](https://huggingface.co/docs/transformers/en/model_doc/higgs_audio_v2).
+Higgs TTS 2 is supported natively in `transformers`: [see the doc](https://huggingface.co/docs/transformers/en/model_doc/higgs_audio_v2).
 
 ```bash
 uv pip install "transformers>=5.3.0"
@@ -600,4 +602,4 @@ You can also check https://github.com/boson-ai/higgs-audio/tree/main/examples fo
 
 ## License
 
-See [LICENSE](./LICENSE)
+See [LICENSE](./LICENSE)