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README.md

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+---
+language:
+- sw
+license: cc-by-nc-4.0
+base_model: facebook/mms-tts-swh
+tags:
+- text-to-speech
+- tts
+- vits
+- swahili
+- mms
+datasets:
+- google/WaxalNLP
+pipeline_tag: text-to-speech
+---
+
+# waxal_swahili-tts-mms
+
+A Swahili text-to-speech model, finetuned from Meta's [MMS-TTS Swahili](https://huggingface.co/facebook/mms-tts-swh) checkpoint on the `swa_tts` split of [google/WaxalNLP](https://huggingface.co/datasets/google/WaxalNLP), using the [VITS finetuning recipe](https://github.com/ylacombe/finetune-hf-vits) from `ylacombe/finetune-hf-vits`.
+
+> **⚠️ Early checkpoint.** This model is saved from step 4,500 of a planned 20,200-step (200-epoch) training run — roughly 22% through. Training was interrupted by free-tier Google Colab resource limits and is being continued on different hardware. Expect rough prosody and occasional mispronunciations. This card will be updated as training progresses.
+
+## Model details
+
+- **Base model:** `facebook/mms-tts-swh` (Meta's Massively Multilingual Speech TTS, Swahili)
+- **Architecture:** VITS (single-speaker)
+- **Training data:** [google/WaxalNLP](https://huggingface.co/datasets/google/WaxalNLP), `swa_tts` config — 1,387 train utterances (805 after duration/length filtering), single Swahili speaker, 16kHz audio, sourced via the Loud & Clear initiative.
+- **Training checkpoint:** step 4,500 / 20,200 planned steps (~22 epochs of ~805 examples)
+- **Sample rate:** 16,000 Hz
+- **Language:** Swahili (`swh` / ISO 639-3)
+
+## Training configuration
+
+| Setting | Value |
+|---|---|
+| Learning rate | 2e-5 |
+| Batch size | 8 |
+| Precision | fp16 |
+| Max clip duration | 20s |
+| Min clip duration | 0.5s |
+| Loss weights | mel=35, kl=1.5, disc=3, gen/fmaps/duration=1 |
+
+Training used the `finetune-hf-vits` recipe with `transformers==4.35.1`, `datasets==2.14.7`, `accelerate==0.24.1`, `numpy<2.0` (this specific combination was required to avoid dependency-incompatibility crashes on Colab).
+
+## Usage
+
+```python
+import numpy as np
+from transformers import pipeline
+import scipy.io.wavfile
+
+synthesiser = pipeline("text-to-speech", model="Mwau/waxal_swahili-tts-mms")
+speech = synthesiser("Habari yako, karibu Kenya.")
+
+audio = np.squeeze(speech["audio"])  # squeeze extra dims before saving/playing
+scipy.io.wavfile.write("output.wav", rate=speech["sampling_rate"], data=audio)
+```
+
+Verified loading cleanly with `transformers` `pipeline("text-to-speech", ...)` — no missing/unexpected weight warnings on load.
+
+## Intended use
+
+Research and experimentation with Swahili TTS, particularly cross-lingual/low-resource TTS transfer techniques. Not yet suitable for production or user-facing applications given the early training stage.
+
+## Limitations
+
+- **Undertrained**: only ~22 epochs completed of a 200-epoch plan; expect unstable prosody, occasional artifacts, and inconsistent pacing.
+- **Single speaker, single dataset**: trained on one speaker's recordings from one dataset; may not generalize well to varied Swahili dialects, accents, or speaking styles.
+- **No formal evaluation yet**: no MOS, WER, or MCD scores have been computed for this checkpoint. Qualitative spot-checks against WAXAL's held-out `test` split suggest intelligible but rough output.
+- **Small dataset**: ~800 training utterances after filtering is a small corpus for TTS; may overfit to the training speaker's specific vocal characteristics.
+
+## License
+
+This model is derived from `facebook/mms-tts-swh`, which is released under **CC-BY-NC-4.0** (non-commercial). This finetuned model inherits that license. The `swa_tts` training data is released under CC-BY-SA-4.0.
+
+## Acknowledgements
+
+- [Meta MMS](https://huggingface.co/facebook/mms-tts-swh) for the base checkpoint
+- [WAXAL / google/WaxalNLP](https://huggingface.co/datasets/google/WaxalNLP) for the training data
+- [ylacombe/finetune-hf-vits](https://github.com/ylacombe/finetune-hf-vits) for the finetuning recipe

added_tokens.json

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+{
+  "<unk>": 39
+}

config.json

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+{
+  "_name_or_path": "Mwau/mms-tts-swh-with-discriminator",
+  "activation_dropout": 0.1,
+  "architectures": [
+    "VitsModel"
+  ],
+  "attention_dropout": 0.1,
+  "depth_separable_channels": 2,
+  "depth_separable_num_layers": 3,
+  "discriminator_kernel_size": 5,
+  "discriminator_period_channels": [
+    1,
+    32,
+    128,
+    512,
+    1024
+  ],
+  "discriminator_periods": [
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+    3,
+    5,
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+  "discriminator_scale_channels": [
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+    16,
+    64,
+    256,
+    1024
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+  "discriminator_stride": 3,
+  "dtype": "float32",
+  "duration_predictor_dropout": 0.5,
+  "duration_predictor_filter_channels": 256,
+  "duration_predictor_flow_bins": 10,
+  "duration_predictor_kernel_size": 3,
+  "duration_predictor_num_flows": 4,
+  "duration_predictor_tail_bound": 5.0,
+  "ffn_dim": 768,
+  "ffn_kernel_size": 3,
+  "flow_size": 192,
+  "hidden_act": "relu",
+  "hidden_dropout": 0.1,
+  "hidden_size": 192,
+  "hop_length": 256,
+  "initializer_range": 0.02,
+  "layer_norm_eps": 1e-05,
+  "layerdrop": 0.1,
+  "leaky_relu_slope": 0.1,
+  "model_type": "vits",
+  "noise_scale": 0.667,
+  "noise_scale_duration": 0.8,
+  "num_attention_heads": 2,
+  "num_hidden_layers": 6,
+  "num_speakers": 1,
+  "posterior_encoder_num_wavenet_layers": 16,
+  "prior_encoder_num_flows": 4,
+  "prior_encoder_num_wavenet_layers": 4,
+  "resblock_dilation_sizes": [
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+  "resblock_kernel_sizes": [
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+  "sampling_rate": 16000,
+  "segment_size": 8192,
+  "speaker_embedding_size": 0,
+  "speaking_rate": 1.0,
+  "spectrogram_bins": 513,
+  "torch_dtype": "float32",
+  "transformers_version": "4.35.1",
+  "upsample_initial_channel": 512,
+  "upsample_kernel_sizes": [
+    16,
+    16,
+    4,
+    4
+  ],
+  "upsample_rates": [
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+  "use_bias": true,
+  "use_stochastic_duration_prediction": true,
+  "vocab_size": 39,
+  "wavenet_dilation_rate": 1,
+  "wavenet_dropout": 0.0,
+  "wavenet_kernel_size": 5,
+  "window_size": 4
+}

model.safetensors

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+oid sha256:0493b6cf3bfe435095a2f53685be564753f981cade1a57b7e7955a485d45a4ef
+size 145231480

preprocessor_config.json

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+{
+  "feature_extractor_type": "VitsFeatureExtractor",
+  "feature_size": 80,
+  "hop_length": 256,
+  "max_wav_value": 32768.0,
+  "n_fft": 1024,
+  "padding_side": "right",
+  "padding_value": 0.0,
+  "return_attention_mask": false,
+  "sampling_rate": 16000
+}

special_tokens_map.json

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+{
+  "pad_token": {
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+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
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+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer_config.json

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+{
+  "add_blank": true,
+  "added_tokens_decoder": {
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+      "lstrip": false,
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+      "single_word": false,
+      "special": true
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+  },
+  "backend": "custom",
+  "clean_up_tokenization_spaces": true,
+  "is_local": false,
+  "is_uroman": false,
+  "language": "swh",
+  "local_files_only": false,
+  "model_max_length": 1000000000000000019884624838656,
+  "normalize": true,
+  "pad_token": "2",
+  "phonemize": false,
+  "tokenizer_class": "VitsTokenizer",
+  "unk_token": "<unk>",
+  "verbose": false
+}

vocab.json

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+  "'": 36,
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+  "v": 21,
+  "w": 16,
+  "y": 30,
+  "z": 28,
+  "ʼ": 35
+}