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README.md ADDED
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+ ---
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+ license: apache-2.0
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+ language:
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+ - ar
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+ base_model: openbmb/VoxCPM2
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+ pipeline_tag: text-to-speech
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+ tags:
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+ - text-to-speech
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+ - arabic
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+ - najdi
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+ - saudi
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+ - voxcpm
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+ - diacritics
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+ - tashkeel
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+ ---
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+
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+ # VoxCPM2 Najdi Arabic TTS (diacritized) — final
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+
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+ Full fine-tune of **VoxCPM2** (2.29B) for **Saudi Najdi Arabic** TTS, trained on
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+ *diacritized* text so that harakat control pronunciation at inference time.
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+
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+ **This checkpoint: step 18264 — final, end of epoch 1** (val loss 0.7848).
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+
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+ ## Why diacritics, and why a full fine-tune
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+
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+ VoxCPM2's tokenizer has no diacritic entries, so every harakat falls through to a
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+ byte-fallback pair (`<0xD9><0x8E>` = fatha) whose embedding rows carry a
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+ fusha/Quranic association from pretraining. Token count inflates x1.96 on marked
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+ text.
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+
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+ A LoRA run on this data could not fix that: `apply_lora_to_named_linear_modules`
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+ wraps **Linear only** (q/k/v/o_proj) and freezes everything else, so
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+ `nn.Embedding` is structurally untouchable — attention can only reinterpret
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+ those rows in context, never change them. Full fine-tuning unfreezes everything
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+ except the AudioVAE, so the rows themselves retrain.
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+
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+ ## Training data
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+
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+ 292,217 rows / 515.8 h, derived from a 377 h / ~1,032-speaker Najdi YouTube corpus:
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+
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+ | portion | rows | note |
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+ |---|---|---|
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+ | diacritized | 204,552 | coverage mean 0.804 |
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+ | long-form (>25 s), bare | 3,835 | diacritization degrades past ~25 s |
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+ | bare **duplicates** of diacritized clips | 83,830 | same audio, marks stripped |
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+
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+ **70.5%** of rows carry marks; 45.0% carry a same-speaker `ref_audio`.
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+
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+ The bare duplicates are the load-bearing part. If bare and marked text sat on
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+ disjoint audio, the model could satisfy both by keying on the voice and learn
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+ nothing about marks. Identical audio carrying both forms leaves one consistent
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+ reading: **marks are optional modifiers of the same speech**.
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+
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+ Diacritics come from
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+ [NAMAA-Space/Cohere-Speech-Tashkeel-2B](https://huggingface.co/NAMAA-Space/Cohere-Speech-Tashkeel-2B),
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+ an **acoustic** diacritizer, so harakat reflect how each word was actually said
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+ rather than what a text model guesses — it stays dialectal
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+ (نِتْسَلَّى, حَتْلَاقُوا, حَيَاخُذ). Because it runs ~10% letter WER its output is
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+ **not** used as the transcript: the corpus transcript is the letter backbone and
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+ only marks transfer word-by-word, leaving misheard words bare. Word-final marks
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+ (i'rab / pausal) are stripped; shadda is kept, being gemination rather than a
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+ case ending.
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+
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+ Transcripts are Cohere ASR text, not raw auto-captions.
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+
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+ ## Training
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+
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+ Stock `openbmb/VoxCPM2`, one epoch, 18,264 steps at effective batch 16
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+ (4 x 4 grad-accum), lr 1e-5, weight_decay 0.01, warmup 100, max_grad_norm 1.0,
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+ lambdas 1.0/1.0. Single H200.
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+
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+ Validation loss: 0.9689 (step 0) -> 0.8033 (2k) -> 0.7852 (8k) -> **0.7834 (15k, best)**
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+ -> 0.7848 (18k). Essentially flat past step 8,000.
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+
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+ ## Inference
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+
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+ Output is **48 kHz** (`model.tts_model.sample_rate`), not 16 kHz — writing it
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+ with a 16 kHz header plays ~3x slow and about an octave and a half low.
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+
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+ Short text works through the standard CLI:
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+
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+ ```bash
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+ voxcpm clone --model-path <this-repo> \
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+ --text "وِش رَايِك نِتْقَابَل بَاكِر بِالْمَقْهَى اللِّي جَنْب الْبَيْت" \
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+ --reference-audio ref.wav --no-denoiser --output out.wav
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+ ```
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+
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+ **Long text must be chunked.** A single autoregressive pass past ~20 s degenerates
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+ into syllable babble in its tail. Measured on a 47 s single-shot generation:
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+ transcribe-back WER 0.41-0.48, with output like
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+ `بي بي بييسوشايشاي ... بيطا بيطا ويطبي طبيطبير`. Insertions and deletions were
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+ both zero, so this is accumulated drift, not a stop-head failure.
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+
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+ Splitting on sentence boundaries fixes it:
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+
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+ | split | chunks | transcribe-back WER |
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+ |---|---|---|
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+ | single-shot 47 s | 1 | 0.410-0.476 |
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+ | comma + word fallback | 8 | 0.105 |
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+ | full stops / question marks only | 16 | **0.069** |
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+
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+ Chunks up to ~15 s measured clean (WER 0.040-0.100). `cfg_value` 2.0 beat 1.5 and
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+ 1.3 (0.250 vs 0.500) — do not lower it.
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+
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+ ## Known limitations
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+
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+ - **Inference text must be diacritized in the same convention as training**:
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+ pausal, word-final marks stripped, dialectal vowels. Text carrying standard MSA
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+ i'rab endings is off-distribution. There is no text-side diacritizer in this
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+ stack — the one used to build the data is acoustic and needs audio.
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+ - Mark coverage is ~80%, so sentences are internally mixed (some words bare).
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+ That is realistic input, not a defect.
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+ - Reference audio in training is capped at 10.0 s; longer references are
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+ untested territory.
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+ - Trained on Najdi. The gaf (ق pronounced /g/) is a deliberate register target,
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+ not an error.
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+ "content": "<|execute_start|>",
151
+ "lstrip": false,
152
+ "normalized": false,
153
+ "rstrip": false,
154
+ "single_word": false,
155
+ "special": true
156
+ },
157
+ "73444": {
158
+ "content": "<|execute_end|>",
159
+ "lstrip": false,
160
+ "normalized": false,
161
+ "rstrip": false,
162
+ "single_word": false,
163
+ "special": true
164
+ },
165
+ "73445": {
166
+ "content": "<|fim_prefix|>",
167
+ "lstrip": false,
168
+ "normalized": false,
169
+ "rstrip": false,
170
+ "single_word": false,
171
+ "special": true
172
+ },
173
+ "73446": {
174
+ "content": "<|fim_middle|>",
175
+ "lstrip": false,
176
+ "normalized": false,
177
+ "rstrip": false,
178
+ "single_word": false,
179
+ "special": true
180
+ },
181
+ "73447": {
182
+ "content": "<|fim_suffix|>",
183
+ "lstrip": false,
184
+ "normalized": false,
185
+ "rstrip": false,
186
+ "single_word": false,
187
+ "special": true
188
+ }
189
+ },
190
+ "additional_special_tokens": [
191
+ "<|im_end|>",
192
+ "<|im_start|>",
193
+ "<|tool_call|>",
194
+ "<|execute_start|>",
195
+ "<|execute_end|>",
196
+ "<|fim_prefix|>",
197
+ "<|fim_middle|>",
198
+ "<|fim_suffix|>"
199
+ ],
200
+ "bos_token": "<s>",
201
+ "clean_up_tokenization_spaces": false,
202
+ "eos_token": "<|im_end|>",
203
+ "legacy": true,
204
+ "model_max_length": 1000000000000000019884624838656,
205
+ "pad_token": null,
206
+ "sp_model_kwargs": {},
207
+ "spaces_between_special_tokens": false,
208
+ "tokenizer_class": "VoxCPM2Tokenizer",
209
+ "unk_token": "<unk>",
210
+ "use_default_system_prompt": false,
211
+ "chat_template": "{% for message in messages %}{{'<|im_start|>' + message['role'] + '\n' + message['content'] + '<|im_end|>' + '\n'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\n' }}{% endif %}",
212
+ "auto_map": {
213
+ "AutoTokenizer": [
214
+ "tokenization_voxcpm2.VoxCPM2Tokenizer",
215
+ null
216
+ ]
217
+ }
218
+ }