初始化项目，由ModelHub XC社区提供模型

Model: maya-research/Veena
Source: Original Platform

.gitattributes

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+tokenizer_config.json filter=lfs diff=lfs merge=lfs -text
+tokenizer.json filter=lfs diff=lfs merge=lfs -text

README.md

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+---
+license: apache-2.0
+language:
+- en
+- hi
+library_name: transformers
+tags:
+- text-to-speech
+- tts
+- hindi
+- english
+- llama
+- audio
+- speech
+- india
+datasets:
+- proprietary
+pipeline_tag: text-to-speech
+co2_eq_emissions:
+  emissions: 0
+  source: "Not specified"
+  training_type: "unknown"
+  geographical_location: "unknown"
+---
+
+# Veena - Text to Speech for Indian Languages
+
+Veena is a state-of-the-art neural text-to-speech (TTS) model developed by Maya Research, designed for English and Indian languages. Built on a Llama architecture backbone, Veena generates natural, expressive speech with emotional tone, remarkable quality, and ultra-low latency. It represents the foundation of our voice intelligence work, bringing human-like voice to the two most spoken languages in the world.
+## Model Overview
+
+**Veena** is a 3B parameter autoregressive transformer model based on the Llama architecture. It is designed to synthesize high-quality speech from text in Hindi and English, including code-mixed scenarios. The model outputs audio at a 24kHz sampling rate using the SNAC neural codec.
+
+* **Model type:** Autoregressive Transformer
+* **Base Architecture:** Llama (3B parameters)
+* **Languages:** Hindi, English
+* **Audio Codec:** SNAC @ 24kHz
+* **License:** Apache 2.0
+* **Developed by:** Maya Research
+* **Model URL:** [https://huggingface.co/maya-research/veena](https://huggingface.co/maya-research/veena)
+
+## Key Features
+
+* **4 Distinct Voices:** `kavya`, `agastya`, `maitri`, and `vinaya` - each with unique vocal characteristics.
+* **Multilingual Support:** Native Hindi and English capabilities with code-mixed support.
+* **Ultra-Fast Inference:** Sub-80ms latency on H100-80GB GPUs.
+* **High-Quality Audio:** 24kHz output with the SNAC neural codec.
+* **Production-Ready:** Optimized for real-world deployment with 4-bit quantization support.
+
+## How to Get Started with the Model
+
+### Installation
+
+To use Veena, you need to install the `transformers`, `torch`, `torchaudio`, `snac`, and `bitsandbytes` libraries.
+
+```bash
+pip install transformers torch torchaudio
+pip install snac bitsandbytes  # For audio decoding and quantization
+```
+
+### Basic Usage
+
+The following Python code demonstrates how to generate speech from text using Veena with 4-bit quantization for efficient inference.
+
+```python
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
+from snac import SNAC
+import soundfile as sf
+
+# Model configuration for 4-bit inference
+quantization_config = BitsAndBytesConfig(
+    load_in_4bit=True,
+    bnb_4bit_quant_type="nf4",
+    bnb_4bit_compute_dtype=torch.bfloat16,
+    bnb_4bit_use_double_quant=True,
+)
+
+# Load model and tokenizer
+model = AutoModelForCausalLM.from_pretrained(
+    "maya-research/veena-tts",
+    quantization_config=quantization_config,
+    device_map="auto",
+    trust_remote_code=True,
+)
+tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)
+
+# Initialize SNAC decoder
+snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()
+
+# Control token IDs (fixed for Veena)
+START_OF_SPEECH_TOKEN = 128257
+END_OF_SPEECH_TOKEN = 128258
+START_OF_HUMAN_TOKEN = 128259
+END_OF_HUMAN_TOKEN = 128260
+START_OF_AI_TOKEN = 128261
+END_OF_AI_TOKEN = 128262
+AUDIO_CODE_BASE_OFFSET = 128266
+
+# Available speakers
+speakers = ["kavya", "agastya", "maitri", "vinaya"]
+
+def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
+    """Generate speech from text using specified speaker voice"""
+
+    # Prepare input with speaker token
+    prompt = f"<spk_{speaker}> {text}"
+    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)
+
+    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
+    input_tokens = [
+        START_OF_HUMAN_TOKEN,
+        *prompt_tokens,
+        END_OF_HUMAN_TOKEN,
+        START_OF_AI_TOKEN,
+        START_OF_SPEECH_TOKEN
+    ]
+
+    input_ids = torch.tensor([input_tokens], device=model.device)
+
+    # Calculate max tokens based on text length
+    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)
+
+    # Generate audio tokens
+    with torch.no_grad():
+        output = model.generate(
+            input_ids,
+            max_new_tokens=max_tokens,
+            do_sample=True,
+            temperature=temperature,
+            top_p=top_p,
+            repetition_penalty=1.05,
+            pad_token_id=tokenizer.pad_token_id,
+            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
+        )
+
+    # Extract SNAC tokens
+    generated_ids = output[0][len(input_tokens):].tolist()
+    snac_tokens = [
+        token_id for token_id in generated_ids
+        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
+    ]
+
+    if not snac_tokens:
+        raise ValueError("No audio tokens generated")
+
+    # Decode audio
+    audio = decode_snac_tokens(snac_tokens, snac_model)
+    return audio
+
+def decode_snac_tokens(snac_tokens, snac_model):
+    """De-interleave and decode SNAC tokens to audio"""
+    if not snac_tokens or len(snac_tokens) % 7 != 0:
+        return None
+
+    # Get the device of the SNAC model. Fixed by Shresth to run on colab notebook :)
+    snac_device = next(snac_model.parameters()).device
+
+    # De-interleave tokens into 3 hierarchical levels
+    codes_lvl = [[] for _ in range(3)]
+    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]
+
+    for i in range(0, len(snac_tokens), 7):
+        # Level 0: Coarse (1 token)
+        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
+        # Level 1: Medium (2 tokens)
+        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
+        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
+        # Level 2: Fine (4 tokens)
+        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
+        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
+        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
+        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])
+
+    # Convert to tensors for SNAC decoder
+    hierarchical_codes = []
+    for lvl_codes in codes_lvl:
+        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_device).unsqueeze(0)
+        if torch.any((tensor < 0) | (tensor > 4095)):
+            raise ValueError("Invalid SNAC token values")
+        hierarchical_codes.append(tensor)
+
+    # Decode with SNAC
+    with torch.no_grad():
+        audio_hat = snac_model.decode(hierarchical_codes)
+
+    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()
+
+# --- Example Usage ---
+
+# Hindi
+text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
+audio = generate_speech(text_hindi, speaker="kavya")
+sf.write("output_hindi_kavya.wav", audio, 24000)
+
+# English
+text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
+audio = generate_speech(text_english, speaker="agastya")
+sf.write("output_english_agastya.wav", audio, 24000)
+
+# Code-mixed
+text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
+audio = generate_speech(text_mixed, speaker="maitri")
+sf.write("output_mixed_maitri.wav", audio, 24000)
+```
+
+## Uses
+
+Veena is ideal for a wide range of applications requiring high-quality, low-latency speech synthesis for Indian languages, including:
+
+  * **Accessibility:** Screen readers and voice-enabled assistance for visually impaired users.
+  * **Customer Service:** IVR systems, voice bots, and automated announcements.
+  * **Content Creation:** Dubbing for videos, e-learning materials, and audiobooks.
+  * **Automotive:** In-car navigation and infotainment systems.
+  * **Edge Devices:** Voice-enabled smart devices and IoT applications.
+
+## Technical Specifications
+
+### Architecture
+
+Veena leverages a 3B parameter transformer-based architecture with several key innovations:
+
+  * **Base Architecture:** Llama-style autoregressive transformer (3B parameters)
+  * **Audio Codec:** SNAC (24kHz) for high-quality audio token generation
+  * **Speaker Conditioning:** Special speaker tokens (`<spk_kavya>`, `<spk_agastya>`, `<spk_maitri>`, `<spk_vinaya>`)
+  * **Parameter-Efficient Training:** LoRA adaptation with differentiated ranks for attention and FFN modules.
+  * **Context Length:** 2048 tokens
+
+### Training
+
+#### Training Infrastructure
+
+  * **Hardware:** 8× NVIDIA H100 80GB GPUs
+  * **Distributed Training:** DDP with optimized communication
+  * **Precision:** BF16 mixed precision training with gradient checkpointing
+  * **Memory Optimization:** 4-bit quantization with NF4 + double quantization
+
+#### Training Configuration
+
+  * **LoRA Configuration:**
+      * `lora_rank_attention`: 192
+      * `lora_rank_ffn`: 96
+      * `lora_alpha`: 2× rank (384 for attention, 192 for FFN)
+      * `lora_dropout`: 0.05
+      * `target_modules`: `["q_proj", "k_proj", "v_proj", "o_proj", "gate_proj", "up_proj", "down_proj"]`
+      * `modules_to_save`: `["embed_tokens"]`
+  * **Optimizer Configuration:**
+      * `optimizer`: AdamW (8-bit)
+      * `optimizer_betas`: (0.9, 0.98)
+      * `optimizer_eps`: 1e-5
+      * `learning_rate_peak`: 1e-4
+      * `lr_scheduler`: cosine
+      * `warmup_ratio`: 0.02
+  * **Batch Configuration:**
+      * `micro_batch_size`: 8
+      * `gradient_accumulation_steps`: 4
+      * `effective_batch_size`: 256
+
+#### Training Data
+
+Veena was trained on **proprietary, high-quality datasets** specifically curated for Indian language TTS.
+
+  * **Data Volume:** 15,000+ utterances per speaker (60,000+ total)
+  * **Languages:** Native Hindi and English utterances with code-mixed support
+  * **Speaker Diversity:** 4 professional voice artists with distinct characteristics
+  * **Audio Quality:** Studio-grade recordings at 24kHz sampling rate
+  * **Content Diversity:** Conversational, narrative, expressive, and informational styles
+
+**Note:** The training datasets are proprietary and not publicly available.
+
+## Performance Benchmarks
+
+| Metric                | Value                     |
+| --------------------- | ------------------------- |
+| Latency (H100-80GB)   | \<80ms                     |
+| Latency (A100-40GB)   | \~120ms                    |
+| Latency (RTX 4090)    | \~200ms                    |
+| Real-time Factor      | 0.05x                     |
+| Throughput            | \~170k tokens/s (8×H100)   |
+| Audio Quality (MOS)   | 4.2/5.0                   |
+| Speaker Similarity    | 92%                       |
+| Intelligibility       | 98%                       |
+
+## Risks, Limitations and Biases
+
+  * **Language Support:** Currently supports only Hindi and English. Performance on other Indian languages is not guaranteed.
+  * **Speaker Diversity:** Limited to 4 speaker voices, which may not represent the full diversity of Indian accents and dialects.
+  * **Hardware Requirements:** Requires a GPU for real-time or near-real-time inference. CPU performance will be significantly slower.
+  * **Input Length:** The model is limited to a maximum input length of 2048 tokens.
+  * **Bias:** The model's performance and voice characteristics are a reflection of the proprietary training data. It may exhibit biases present in the data.
+
+## Future Updates
+
+We are actively working on expanding Veena's capabilities:
+
+  * Support for Tamil, Telugu, Bengali, Marathi, and other Indian languages.
+  * Additional speaker voices with regional accents.
+  * Emotion and prosody control tokens.
+  * Streaming inference support.
+  * CPU optimization for edge deployment.
+
+## Citing
+
+If you use Veena in your research or applications, please cite:
+
+```bibtex
+@misc{veena2025,
+  title={Veena: Open Source Text-to-Speech for Indian Languages},
+  author={Maya Research Team},
+  year={2025},
+  publisher={HuggingFace},
+  url={[https://huggingface.co/maya-research/veena-tts](https://huggingface.co/maya-research/veena-tts)}
+}
+```
+
+## Acknowledgments
+
+We thank the open-source community and all contributors who made this project possible. Special thanks to the voice artists who provided high-quality recordings for training.

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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+{
+  "architectures": [
+    "LlamaForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 128000,
+  "eos_token_id": 128001,
+  "head_dim": 128,
+  "hidden_act": "silu",
+  "hidden_size": 3072,
+  "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,
+  "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": "4.52.4",
+  "use_cache": true,
+  "vocab_size": 156951
+}

configuration.json

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+{"framework": "pytorch", "task": "text-to-speech", "allow_remote": true}

generation_config.json

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+{
+  "_from_model_config": true,
+  "bos_token_id": 128000,
+  "do_sample": true,
+  "eos_token_id": 128001,
+  "temperature": 0.6,
+  "top_p": 0.9,
+  "transformers_version": "4.52.4"
+}

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special_tokens_map.json

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