Initial bf16 MLX conversion of kenpath/svara-tts-v1

.gitattributes

@@ -33,3 +33,4 @@ 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
+tokenizer.json filter=lfs diff=lfs merge=lfs -text

README.md

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+---
+base_model: kenpath/svara-tts-v1
+models:
+- kenpath/svara-tts-v1
+- canopylabs/3b-hi-ft-research_release
+- mlx-community/svara-tts-v1-4bit
+- mlx-community/svara-tts-v1-8bit
+license: apache-2.0
+language:
+- hi
+- bn
+- mr
+- te
+- kn
+- bho
+- mag
+- hne
+- mai
+- as
+- brx
+- doi
+- gu
+- ml
+- pa
+- ta
+- ne
+- sa
+- en
+tags:
+- text-to-speech
+- speech-synthesis
+- multilingual
+- indic
+- orpheus
+- snac
+- mlx
+- mlx-audio
+task_categories:
+- text-to-speech
+pipeline_tag: text-to-speech
+pretty_name: Svara-TTS v1 (MLX, bfloat16)
+datasets:
+- SYSPIN
+- RASA
+- IndicTTS
+- SPICOR
+library_name: mlx
+---
+
+# Svara-TTS v1 — MLX bfloat16
+
+> **Parent model:** [`kenpath/svara-tts-v1`](https://huggingface.co/kenpath/svara-tts-v1) — full upstream weights, model card, training data, and evaluation. All credit for the model itself goes to the [Kenpath](https://huggingface.co/kenpath) team. This repo only contains an MLX-format conversion for inference on Apple Silicon.
+>
+> **Orpheus base:** [`canopylabs/3b-hi-ft-research_release`](https://huggingface.co/canopylabs/3b-hi-ft-research_release) — Canopy Labs' Orpheus Hindi research release, which Svara was fine-tuned from.
+
+Full-precision (bfloat16) MLX port of [`kenpath/svara-tts-v1`](https://huggingface.co/kenpath/svara-tts-v1) — an autoregressive multilingual text-to-speech model for 19 Indian languages, in the Orpheus / SNAC family. Same numerical precision as upstream, repackaged in MLX-native format (~6.6 GB sharded safetensors).
+
+For smaller memory footprints, use the 4-bit or 8-bit quantized variants linked below.
+
+Built for [mlx-audio](https://github.com/Blaizzy/mlx-audio) on Apple Silicon.
+
+## Usage
+
+Requires `mlx-audio` with TTS extras:
+
+```bash
+pip install "mlx-audio[tts]"
+```
+
+### Python
+
+```python
+import numpy as np
+import soundfile as sf
+import mlx.core as mx
+from mlx_audio.tts.utils import load_model
+
+model = load_model("shreyask/svara-tts-v1")
+
+chunks = []
+for result in model.generate(
+    text="नमस्ते, आप कैसे हैं? मैं ठीक हूँ।",
+    voice="Hindi (Female)",
+    temperature=0.75,
+    top_p=0.9,
+    top_k=40,
+    repetition_penalty=1.1,
+    max_tokens=1200,
+):
+    chunks.append(result.audio)
+
+audio = mx.concatenate(chunks, axis=0)
+sf.write("hello_hi.wav", np.asarray(audio), model.sample_rate)  # 24 kHz
+```
+
+### CLI
+
+```bash
+mlx_audio.tts.generate \
+    --model shreyask/svara-tts-v1 \
+    --text "नमस्ते, आप कैसे हैं?" \
+    --voice "Hindi (Female)" \
+    --temperature 0.75 \
+    --top_p 0.9
+```
+
+## Voices
+
+Use a string of the form `"<Language Name> (<Gender>)"`:
+
+| Language     | Voices                              |
+|--------------|-------------------------------------|
+| Hindi        | `Hindi (Male)`, `Hindi (Female)`    |
+| Bengali      | `Bengali (Male)`, `Bengali (Female)`|
+| Marathi      | `Marathi (Male)`, `Marathi (Female)`|
+| Telugu       | `Telugu (Male)`, `Telugu (Female)`  |
+| Kannada      | `Kannada (Male)`, `Kannada (Female)`|
+| Tamil        | `Tamil (Male)`, `Tamil (Female)`    |
+| Malayalam    | `Malayalam (Male)`, `Malayalam (Female)` |
+| Gujarati     | `Gujarati (Male)`, `Gujarati (Female)` |
+| Punjabi      | `Punjabi (Male)`, `Punjabi (Female)` |
+| Assamese     | `Assamese (Male)`, `Assamese (Female)` |
+| Bhojpuri     | `Bhojpuri (Male)`, `Bhojpuri (Female)` |
+| Magahi       | `Magahi (Male)`, `Magahi (Female)`  |
+| Maithili     | `Maithili (Male)`, `Maithili (Female)` |
+| Chhattisgarhi| `Chhattisgarhi (Male)`, `Chhattisgarhi (Female)` |
+| Bodo         | `Bodo (Male)`, `Bodo (Female)`      |
+| Dogri        | `Dogri (Male)`, `Dogri (Female)`    |
+| Nepali       | `Nepali (Male)`, `Nepali (Female)`  |
+| Sanskrit     | `Sanskrit (Male)`, `Sanskrit (Female)` |
+| English (Indian) | `English (Indian) (Male)`, `English (Indian) (Female)` |
+
+Total: **38 voices** across 19 languages.
+
+## Sampling Recommendations
+
+The upstream `svara-tts-inference` repo uses these defaults; they're a good starting point:
+
+| Parameter | Value |
+|-----------|-------|
+| `temperature` | 0.75 |
+| `top_p` | 0.9 |
+| `top_k` | 40 |
+| `repetition_penalty` | 1.1 |
+| `max_tokens` | 1200–2048 |
+
+## Architecture
+
+- **Backbone:** Llama-3.2-3B (fine-tuned from [`canopylabs/3b-hi-ft-research_release`](https://huggingface.co/canopylabs/3b-hi-ft-research_release), Canopy's Orpheus Hindi base).
+- **Codec:** [SNAC 24 kHz](https://huggingface.co/hubertsiuzdak/snac_24khz) — 3-level hierarchical RVQ, 7 codes per ~10 ms frame. Loaded automatically by `mlx-audio`.
+- **Output:** 24 kHz mono PCM.
+
+## Other Quants
+
+- 8-bit MLX: [`mlx-community/svara-tts-v1-8bit`](https://huggingface.co/mlx-community/svara-tts-v1-8bit) (~3.5 GB)
+- 4-bit MLX: [`mlx-community/svara-tts-v1-4bit`](https://huggingface.co/mlx-community/svara-tts-v1-4bit) (~1.9 GB)
+
+## License
+
+Apache 2.0 — see [base model card](https://huggingface.co/kenpath/svara-tts-v1) for full details.

chat_template.jinja

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+{{- bos_token }}
+{%- if custom_tools is defined %}
+    {%- set tools = custom_tools %}
+{%- endif %}
+{%- if not tools_in_user_message is defined %}
+    {%- set tools_in_user_message = true %}
+{%- endif %}
+{%- if not date_string is defined %}
+    {%- if strftime_now is defined %}
+        {%- set date_string = strftime_now("%d %b %Y") %}
+    {%- else %}
+        {%- set date_string = "26 Jul 2024" %}
+    {%- endif %}
+{%- endif %}
+{%- if not tools is defined %}
+    {%- set tools = none %}
+{%- endif %}
+
+{#- This block extracts the system message, so we can slot it into the right place. #}
+{%- if messages[0]['role'] == 'system' %}
+    {%- set system_message = messages[0]['content']|trim %}
+    {%- set messages = messages[1:] %}
+{%- else %}
+    {%- set system_message = "" %}
+{%- endif %}
+
+{#- System message #}
+{{- "<|start_header_id|>system<|end_header_id|>\n\n" }}
+{%- if tools is not none %}
+    {{- "Environment: ipython\n" }}
+{%- endif %}
+{{- "Cutting Knowledge Date: December 2023\n" }}
+{{- "Today Date: " + date_string + "\n\n" }}
+{%- if tools is not none and not tools_in_user_message %}
+    {{- "You have access to the following functions. To call a function, please respond with JSON for a function call." }}
+    {{- 'Respond in the format {"name": function name, "parameters": dictionary of argument name and its value}.' }}
+    {{- "Do not use variables.\n\n" }}
+    {%- for t in tools %}
+        {{- t | tojson(indent=4) }}
+        {{- "\n\n" }}
+    {%- endfor %}
+{%- endif %}
+{{- system_message }}
+{{- "<|eot_id|>" }}
+
+{#- Custom tools are passed in a user message with some extra guidance #}
+{%- if tools_in_user_message and not tools is none %}
+    {#- Extract the first user message so we can plug it in here #}
+    {%- if messages | length != 0 %}
+        {%- set first_user_message = messages[0]['content']|trim %}
+        {%- set messages = messages[1:] %}
+    {%- else %}
+        {{- raise_exception("Cannot put tools in the first user message when there's no first user message!") }}
+{%- endif %}
+    {{- '<|start_header_id|>user<|end_header_id|>\n\n' -}}
+    {{- "Given the following functions, please respond with a JSON for a function call " }}
+    {{- "with its proper arguments that best answers the given prompt.\n\n" }}
+    {{- 'Respond in the format {"name": function name, "parameters": dictionary of argument name and its value}.' }}
+    {{- "Do not use variables.\n\n" }}
+    {%- for t in tools %}
+        {{- t | tojson(indent=4) }}
+        {{- "\n\n" }}
+    {%- endfor %}
+    {{- first_user_message + "<|eot_id|>"}}
+{%- endif %}
+
+{%- for message in messages %}
+    {%- if not (message.role == 'ipython' or message.role == 'tool' or 'tool_calls' in message) %}
+        {{- '<|start_header_id|>' + message['role'] + '<|end_header_id|>\n\n'+ message['content'] | trim + '<|eot_id|>' }}
+    {%- elif 'tool_calls' in message %}
+        {%- if not message.tool_calls|length == 1 %}
+            {{- raise_exception("This model only supports single tool-calls at once!") }}
+        {%- endif %}
+        {%- set tool_call = message.tool_calls[0].function %}
+        {{- '<|start_header_id|>assistant<|end_header_id|>\n\n' -}}
+        {{- '{"name": "' + tool_call.name + '", ' }}
+        {{- '"parameters": ' }}
+        {{- tool_call.arguments | tojson }}
+        {{- "}" }}
+        {{- "<|eot_id|>" }}
+    {%- elif message.role == "tool" or message.role == "ipython" %}
+        {{- "<|start_header_id|>ipython<|end_header_id|>\n\n" }}
+        {%- if message.content is mapping or message.content is iterable %}
+            {{- message.content | tojson }}
+        {%- else %}
+            {{- message.content }}
+        {%- endif %}
+        {{- "<|eot_id|>" }}
+    {%- endif %}
+{%- endfor %}
+{%- if add_generation_prompt %}
+    {{- '<|start_header_id|>assistant<|end_header_id|>\n\n' }}
+{%- endif %}

config.json

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+        "LlamaForCausalLM"
+    ],
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+    "attention_dropout": 0.0,
+    "bos_token_id": 128000,
+    "eos_token_id": 128009,
+    "head_dim": 128,
+    "hidden_act": "silu",
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+    "initializer_range": 0.02,
+    "intermediate_size": 8192,
+    "max_position_embeddings": 131072,
+    "mlp_bias": false,
+    "model_type": "llama",
+    "num_attention_heads": 24,
+    "num_hidden_layers": 28,
+    "num_key_value_heads": 8,
+    "pad_token_id": 128263,
+    "pretraining_tp": 1,
+    "rms_norm_eps": 1e-05,
+    "rope_scaling": {
+        "factor": 32.0,
+        "high_freq_factor": 4.0,
+        "low_freq_factor": 1.0,
+        "original_max_position_embeddings": 8192,
+        "rope_type": "llama3"
+    },
+    "rope_theta": 500000.0,
+    "tie_word_embeddings": true,
+    "torch_dtype": "bfloat16",
+    "transformers_version": "4.55.4",
+    "unsloth_version": "2025.10.4",
+    "use_cache": true,
+    "vocab_size": 156940
+}

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