Clean publication: Stage 2 LoRA adapter without checkpoints

.gitattributes

@@ -33,3 +33,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+checkpoint-180/tokenizer.json filter=lfs diff=lfs merge=lfs -text
+tokenizer.json filter=lfs diff=lfs merge=lfs -text

README.md

@@ -1,3 +1,132 @@
----
-license: apache-2.0
----
+---
+license: apache-2.0
+base_model: Qwen/Qwen2.5-7B
+library_name: peft
+tags:
+- text-to-speech
+- ssml
+- french
+- qwen2.5
+- lora
+---
+# ssml-break2ssml-fr-lora
+
+This is the second-stage LoRA adapter for **French SSML generation**, converting *pause-annotated text* into full SSML markup with `<break>` tags.
+
+This model is part of the cascade described in the paper:
+
+**"Improving French Synthetic Speech Quality via SSML Prosody Control"**
+Nassima Ould-Ouali, Éric Moulines – *ICNLSP 2025 (Springer LNCS)* [accepted].
+
+
+---
+
+
+## 🧠 Model Details
+
+- **Base model**: [`Qwen/Qwen2.5-7B`](https://huggingface.co/Qwen/Qwen2.5-7B)
+- **Adapter method**: LoRA (Low-Rank Adaptation via [`peft`](https://github.com/huggingface/peft))
+- **LoRA rank**: 8 — **Alpha**: 16
+- **Training**: 5 epochs, batch size 1 (gradient accumulation)
+- **Languages**: French
+- **Model size**: 7B (adapter-only)
+- **License**: Apache 2.0
+
+---
+
+## 🧩 Pipeline Overview
+
+This model is part of a two-stage SSML cascade for improving French TTS prosody:
+
+| Step | Model                                     | Description                                  |
+|------|-------------------------------------------|----------------------------------------------|
+| 1️⃣   | `nassimaODL/ssml-text2breaks-fr-lora`     | Inserts symbolic pauses like `#250`, `#500`  |
+| 2️⃣   | `nassimaODL/ssml-break2ssml-fr-lora`      | Converts symbols to `<break time="..."/>` SSML |
+
+
+### ✨ Example
+
+```text
+Input:  Bonjour#250 comment vas-tu ?
+Output: Bonjour<break time="250ms"/> comment vas-tu ?
+
+---
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+from peft import PeftModel
+
+tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-7B")
+base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-7B", device_map="auto")
+model = PeftModel.from_pretrained(base_model, "nassimaODL/ssml-break2ssml-fr-lora")
+
+input_text = "Bonjour#250 comment vas-tu ?"
+inputs = tokenizer(input_text, return_tensors="pt").to(model.device)
+
+with torch.no_grad():
+    outputs = model.generate(**inputs, max_new_tokens=128)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))```
+
+--- 
+
+## 🧪 Evaluation Summary
+
+| Metric                    | Value         |
+|--------------------------|---------------|
+| Pause Insertion Accuracy | 87.3%         |
+| RMSE (pause duration)    | 98.5 ms       |
+| MOS gain (vs. baseline)  | +0.42         |
+
+Evaluation was performed on a held-out French validation set with annotated SSML pauses. Mean Opinion Score (MOS) improvements were assessed using TTS outputs rendered with Azure Henri voice and rated by 30 native French speakers.
+
+---
+
+## �� Training Data
+
+This LoRA adapter was trained on a corpus of ~4,500 French utterances. Input texts were annotated with symbolic pause indicators (e.g., `#250` for 250ms), automatically aligned using a combination of Whisper-Kyutai timestamping and F0/syntactic heuristics.
+
+Annotations were refined via a hybrid heuristic rule set combining:
+- Voice activity boundaries (via Auditok)
+- F0 contour analysis (pitch dips before breaks)
+- Syntactic cues (punctuation, conjunctions)
+
+For full details, see our data preparation pipeline on GitHub:  
+🔗 [https://github.com/NassimaOULDOUALI/Prosody-Control-French-TTS](https://github.com/NassimaOULDOUALI/Prosody-Control-French-TTS)
+
+---
+
+## ⚙️ Training Setup
+
+- **Compute**: Jean-Zay (GENCI/IDRIS), A100 80GB x1
+- **Framework**: HuggingFace `transformers` + `peft`
+- **LoRA method**: rank = 8, alpha = 16, dropout = 0.05
+- **Precision**: bf16
+- **Max sequence length**: 768 tokens (256 input + 512 output)
+- **Epochs**: 5
+- **Optimizer**: AdamW (lr = 2e-4, no warmup)
+- **LoRA target modules**:
+  `q_proj`, `k_proj`, `v_proj`, `o_proj`, `gate_proj`, `up_proj`, `down_proj`
+
+Training was performed using the [Unsloth](https://github.com/unslothai/unsloth) SFTTrainer and PEFT adapter injection on Qwen2.5-7B base.
+
+---
+
+## ⚠️ Limitations
+
+- Only `<break>` tags are supported; no pitch, rate, or emphasis control yet.
+- Pause accuracy is sensitive to punctuation and malformed inputs.
+- SSML output has been optimized primarily for Azure voices (e.g., `fr-FR-HenriNeural`). Other engines may interpret `<break>` tags differently.
+- The model assumes the presence of symbolic pause markers in the input (e.g., `#250`). For automatic prediction of such symbols, refer to our stage-1 model:  
+  🔗 [`nassimaODL/ssml-text2breaks-fr-lora`](https://huggingface.co/nassimaODL/ssml-text2breaks-fr-lora)
+
+---
+@inproceedings{ould-ouali2025improving,
+  author    = {Nassima Ould-Ouali and Awais Sani and Tim Luka Horstmann and Jonah Dauvet and Ruben Bueno and Éric Moulines},
+  title     = {Improving French Synthetic Speech Quality via SSML Prosody Control},
+  booktitle = {Proceedings of the 9th International Conference on Natural Language and Speech Processing (ICNLSP)},
+  series    = {Lecture Notes in Computer Science},
+  publisher = {Springer},
+  year      = {2025},
+  note      = {To appear}
+}
+

adapter_config.json

@@ -0,0 +1,39 @@
+{
+  "alpha_pattern": {},
+  "auto_mapping": null,
+  "base_model_name_or_path": "Qwen/Qwen2.5-7B",
+  "bias": "none",
+  "corda_config": null,
+  "eva_config": null,
+  "exclude_modules": null,
+  "fan_in_fan_out": false,
+  "inference_mode": true,
+  "init_lora_weights": true,
+  "layer_replication": null,
+  "layers_pattern": null,
+  "layers_to_transform": null,
+  "loftq_config": {},
+  "lora_alpha": 16,
+  "lora_bias": false,
+  "lora_dropout": 0.1,
+  "megatron_config": null,
+  "megatron_core": "megatron.core",
+  "modules_to_save": null,
+  "peft_type": "LORA",
+  "r": 8,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": [
+    "q_proj",
+    "k_proj",
+    "up_proj",
+    "down_proj",
+    "o_proj",
+    "v_proj",
+    "gate_proj"
+  ],
+  "task_type": "CAUSAL_LM",
+  "trainable_token_indices": null,
+  "use_dora": false,
+  "use_rslora": false
+}

adapter_model.safetensors

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added_tokens.json

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chat_template.jinja

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+{%- if tools %}
+    {{- '<|im_start|>system\n' }}
+    {%- if messages[0]['role'] == 'system' %}
+        {{- messages[0]['content'] }}
+    {%- else %}
+        {{- 'You are a helpful assistant.' }}
+    {%- endif %}
+    {{- "\n\n# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
+    {%- for tool in tools %}
+        {{- "\n" }}
+        {{- tool | tojson }}
+    {%- endfor %}
+    {{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
+{%- else %}
+    {%- if messages[0]['role'] == 'system' %}
+        {{- '<|im_start|>system\n' + messages[0]['content'] + '<|im_end|>\n' }}
+    {%- else %}
+        {{- '<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n' }}
+    {%- endif %}
+{%- endif %}
+{%- for message in messages %}
+    {%- if (message.role == "user") or (message.role == "system" and not loop.first) or (message.role == "assistant" and not message.tool_calls) %}
+        {{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
+    {%- elif message.role == "assistant" %}
+        {{- '<|im_start|>' + message.role }}
+        {%- if message.content %}
+            {{- '\n' + message.content }}
+        {%- endif %}
+        {%- for tool_call in message.tool_calls %}
+            {%- if tool_call.function is defined %}
+                {%- set tool_call = tool_call.function %}
+            {%- endif %}
+            {{- '\n<tool_call>\n{"name": "' }}
+            {{- tool_call.name }}
+            {{- '", "arguments": ' }}
+            {{- tool_call.arguments | tojson }}
+            {{- '}\n</tool_call>' }}
+        {%- endfor %}
+        {{- '<|im_end|>\n' }}
+    {%- elif message.role == "tool" %}
+        {%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != "tool") %}
+            {{- '<|im_start|>user' }}
+        {%- endif %}
+        {{- '\n<tool_response>\n' }}
+        {{- message.content }}
+        {{- '\n</tool_response>' }}
+        {%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
+            {{- '<|im_end|>\n' }}
+        {%- endif %}
+    {%- endif %}
+{%- endfor %}
+{%- if add_generation_prompt %}
+    {{- '<|im_start|>assistant\n' }}
+{%- endif %}

special_tokens_map.json

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tokenizer.json

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