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@@ -5,6 +5,7 @@ We present LFM2-Audio-1.5B, [Liquid AI](https://www.liquid.ai/)'s first end-to-e
 LFM2-Audio supports two generation modes, interleaved and sequential, to maximize performance and quality across different tasks. Interleaved generation outputs text and audio tokens in a fixed interleaved pattern. This approach minimizes time to first audio output and number of tokens generated, making it ideal for naturally flowing real-time speech-to-speech interactions on resource constrained devices. Sequential generation mode, where the model decides when to switch modalities via special tokens, is suitable for non-conversational tasks, such as speech-to-text (ASR) or text-to-speech (TTS).
 
 ### Updates
+- The Japanese version of our model [LiquidAI/LFM2.5-Audio-1.5B-JP](https://huggingface.co/LiquidAI/LFM2.5-Audio-1.5B-JP) is released! It is the first Japanese speech-to-speech model of this scale. To try it out, follow the instructions [here](./README_JP.md).
 - [Finetuning](#finetuning) is now supported in both interleaved and sequential generation modes. Version 1.2.0 introduces data preparation tools and a lightweight trainer, enabling users to fine-tune models for a broad range of tasks, from ASR and TTS to function calling and end-to-end speech-to-speech chat.
 - [LFM2.5-Audio-1.5B](https://huggingface.co/LiquidAI/LFM2.5-Audio-1.5B) is released! This model is based on the stronger LFM2.5-1.2B base, and comes with a lightning fast LFM2 based audio detokenizer, stronger ASR, and better TTS voices. To use the new detokenizer, simply use `processor.decode`, see the examples below for more details. For the improved TTS voices, see the [TTS](#tts) section.
 
diff --git a/README_JP.md b/README_JP.md
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+# Liquid Audio - Japanese Speech-to-Speech models
+
+### Finetuning
+
+To fine-tune our Japanese model on your own data in interleaved generation mode, instantiate the `LFM2AudioChatMapper` class with `interleaved_text_tokens=6` and `interleaved_audio_tokens=9`. These values reflect the predefined Japanese interleaving ratio of 6 text tokens to 9 audio tokens, based on tokenization statistics.
+
+### Multi-turn, multi-modal chat
+
+<details>
+
+<summary>Conversation transcript</summary>
+
+**User**
+
+https://github.com/user-attachments/assets/3c8a2f21-d8e5-4b52-9d8e-9604c5cbb1e6
+
+**Assistant**
+
+こんにちは。私はリキッドリリーと申します。質問に答えたり、アドバイスを提供したりするためのAIボイスアシスタントです。リアルタイムでさまざまな言語タスクをお手伝いするよう設計されています。
+
+https://github.com/user-attachments/assets/345d0837-4f90-4e78-910e-39cd95321cdd
+
+**User**
+
+富士山の高さは何メートルですか。
+
+**Assistant**
+
+富士山の高さは約3,776メートルです。
+
+https://github.com/user-attachments/assets/f0efb661-e01f-4bd2-83fb-77e9e0d9f8d7
+
+</details>
+
+```python
+import torch
+import soundfile as sf
+from liquid_audio import LFM2AudioModel, LFM2AudioProcessor, ChatState, LFMModality
+
+# Load models
+HF_REPO = "LiquidAI/LFM2.5-Audio-1.5B-JP"
+
+processor = LFM2AudioProcessor.from_pretrained(HF_REPO).eval()
+model = LFM2AudioModel.from_pretrained(HF_REPO).eval()
+
+# Set up inputs for the model
+chat = ChatState(processor)
+
+chat.new_turn("system")
+chat.add_text("Respond with interleaved text and audio.")
+chat.end_turn()
+
+chat.new_turn("user")
+wav, sampling_rate = sf.read("assets/question_jp.wav", dtype="float32")
+wav = torch.from_numpy(wav).unsqueeze(0)
+chat.add_audio(wav, sampling_rate)
+chat.end_turn()
+
+chat.new_turn("assistant")
+
+# Generate text and audio tokens.
+text_out: list[torch.Tensor] = []
+audio_out: list[torch.Tensor] = []
+modality_out: list[LFMModality] = []
+for t in model.generate_interleaved(**chat, max_new_tokens=512, audio_temperature=1.0, audio_top_k=4):
+    if t.numel() == 1:
+        print(processor.text.decode(t), end="", flush=True)
+        text_out.append(t)
+        modality_out.append(LFMModality.TEXT)
+    else:
+        audio_out.append(t)
+        modality_out.append(LFMModality.AUDIO_OUT)
+
+# output: こんにちは。私はリキッドリリーと申します。質問に答えたり、アドバイスを提供したりするためのAIボイスアシスタントです。リアルタイムでさまざまな言語タスクをお手伝いするよう設計されています。
+
+# Detokenize audio, removing the last "end-of-audio" codes
+# Mimi returns audio at 24kHz
+audio_codes = torch.stack(audio_out[:-1], 1).unsqueeze(0)
+waveform = processor.decode(audio_codes)
+sf.write("answer_jp1.wav", waveform.cpu()[0], 24_000)
+
+# Append newly generated tokens to chat history
+chat.append(
+    text = torch.stack(text_out, 1),
+    audio_out = torch.stack(audio_out, 1),
+    modality_flag = torch.tensor(modality_out),
+)
+chat.end_turn()
+
+# Start new turn
+chat.new_turn("user")
+chat.add_text("富士山の高さは何メートルですか。")
+chat.end_turn()
+
+chat.new_turn("assistant")
+
+# Generate second turn text and audio tokens.
+audio_out: list[torch.Tensor] = []
+for t in model.generate_interleaved(**chat, max_new_tokens=512, audio_temperature=1.0, audio_top_k=4):
+    if t.numel() == 1:
+        print(processor.text.decode(t), end="", flush=True)
+    else:
+        audio_out.append(t)
+
+# output: 富士山の高さは約3,776メートルです。
+
+# Detokenize second turn audio, removing the last "end-of-audio" codes
+audio_codes = torch.stack(audio_out[:-1], 1).unsqueeze(0)
+waveform = processor.decode(audio_codes)
+sf.write("answer_jp2.wav", waveform.cpu()[0], 24_000)
+```
+
+
+### ASR
+
+For ASR, use the fixed system prompt `Perform ASR in japanese.` instead.
+
+<details>
+
+<summary>Input audio snippet</summary>
+
+https://github.com/user-attachments/assets/be2438d9-b7c7-41ab-bbc9-cd7c505083e4
+
+**Model output**: この度は弊社の確認不足により多大なご迷惑をおかけしましたことを深くお詫び申し上げます。今後はこのようなことが二度と起こらないよう社内のチェック体制を徹底してまいります。
+
+</details>
+
+```python
+import torch
+import soundfile as sf
+from liquid_audio import LFM2AudioModel, LFM2AudioProcessor, ChatState, LFMModality
+
+# Load models
+HF_REPO = "LiquidAI/LFM2.5-Audio-1.5B-JP"
+
+processor = LFM2AudioProcessor.from_pretrained(HF_REPO).eval()
+model = LFM2AudioModel.from_pretrained(HF_REPO).eval()
+
+# Set up inputs for the model
+chat = ChatState(processor)
+
+chat.new_turn("system")
+chat.add_text("Perform ASR in japanese.")
+chat.end_turn()
+
+chat.new_turn("user")
+wav, sampling_rate = sf.read("assets/asr_jp.wav", dtype="float32")
+wav = torch.from_numpy(wav).unsqueeze(0)
+chat.add_audio(wav, sampling_rate)
+chat.end_turn()
+
+chat.new_turn("assistant")
+
+# Generate text
+for t in model.generate_sequential(**chat, max_new_tokens=512):
+    if t.numel() == 1:
+        print(processor.text.decode(t), end="", flush=True)
+
+# Output: この度は弊社の確認不足により多大なご迷惑をおかけしましたことを深くお詫び申し上げます。今後はこのようなことが二度と起こらないよう社内のチェック体制を徹底してまいります。
+```
+
+### TTS
+
+For TTS, use the fixed system prompt `Perform TTS in japanese.` instead.
+
+<details>
+
+<summary>TTS Sample</summary>
+
+**System prompt**: Perform TTS in japanese.
+
+**Input sentence**: 先週ご相談いただいた新しいプロジェクトの件ですが、社内で検討した結果、ぜひ前向きに進めさせていただきたいと考えております。つきましては、具体的なスケジュールについて一度お打ち合わせの機会をいただけますでしょうか。
+
+**Output audio**
+
+https://github.com/user-attachments/assets/ee95f4db-141d-44a5-b592-eb2b6d77a45c
+
+</details>
+
+```python
+import torch
+import soundfile as sf
+from liquid_audio import LFM2AudioModel, LFM2AudioProcessor, ChatState, LFMModality
+
+# Load models
+HF_REPO = "LiquidAI/LFM2.5-Audio-1.5B-JP"
+
+processor = LFM2AudioProcessor.from_pretrained(HF_REPO).eval()
+model = LFM2AudioModel.from_pretrained(HF_REPO).eval()
+
+# Set up inputs for the model
+chat = ChatState(processor)
+
+chat.new_turn("system")
+chat.add_text("Perform TTS in japanese.")
+chat.end_turn()
+
+chat.new_turn("user")
+chat.add_text("先週ご相談いただいた新しいプロジェクトの件ですが、社内で検討した結果、ぜひ前向きに進めさせていただきたいと考えております。つきましては、具体的なスケジュールについて一度お打ち合わせの機会をいただけますでしょうか。")
+chat.end_turn()
+
+chat.new_turn("assistant")
+
+# Generate text
+audio_out: list[torch.Tensor] = []
+for t in model.generate_sequential(**chat, max_new_tokens=512, audio_temperature = 0.8, audio_top_k=64):
+    if t.numel() > 1:
+        audio_out.append(t)
+
+# Detokenize audio
+audio_codes = torch.stack(audio_out[:-1], 1).unsqueeze(0)
+waveform = processor.decode(audio_codes)
+sf.write("tts_jp.wav", waveform.cpu()[0], 24_000)
+```
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