From ef6c6ce051ab79bf3d51149a5892643cb759daa8 Mon Sep 17 00:00:00 2001 From: ModelHub XC Date: Wed, 15 Jul 2026 07:51:06 +0800 Subject: [PATCH] =?UTF-8?q?=E5=88=9D=E5=A7=8B=E5=8C=96=E9=A1=B9=E7=9B=AE?= =?UTF-8?q?=EF=BC=8C=E7=94=B1ModelHub=20XC=E7=A4=BE=E5=8C=BA=E6=8F=90?= =?UTF-8?q?=E4=BE=9B=E6=A8=A1=E5=9E=8B?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Model: maya-research/Veena Source: Original Platform --- .gitattributes | 50 +++++ README.md | 317 +++++++++++++++++++++++++++++++ chat_template.jinja | 93 +++++++++ config.json | 35 ++++ configuration.json | 1 + generation_config.json | 9 + model-00001-of-00002.safetensors | 3 + model-00002-of-00002.safetensors | 3 + model.safetensors.index.json | 262 +++++++++++++++++++++++++ special_tokens_map.json | 36 ++++ tokenizer.json | 3 + tokenizer_config.json | 3 + 12 files changed, 815 insertions(+) create mode 100644 .gitattributes create mode 100644 README.md create mode 100644 chat_template.jinja create mode 100644 config.json create mode 100644 configuration.json create mode 100644 generation_config.json create mode 100644 model-00001-of-00002.safetensors create mode 100644 model-00002-of-00002.safetensors create mode 100644 model.safetensors.index.json create mode 100644 special_tokens_map.json create mode 100644 tokenizer.json create mode 100644 tokenizer_config.json diff --git a/.gitattributes b/.gitattributes new file mode 100644 index 0000000..e2b6026 --- /dev/null +++ b/.gitattributes @@ -0,0 +1,50 @@ +*.7z filter=lfs diff=lfs merge=lfs -text +*.arrow filter=lfs diff=lfs merge=lfs -text +*.bin filter=lfs diff=lfs merge=lfs -text +*.bin.* filter=lfs diff=lfs merge=lfs -text +*.bz2 filter=lfs diff=lfs merge=lfs -text +*.ftz filter=lfs diff=lfs merge=lfs -text +*.gz filter=lfs diff=lfs merge=lfs -text +*.h5 filter=lfs diff=lfs merge=lfs -text +*.joblib filter=lfs diff=lfs merge=lfs -text +*.lfs.* filter=lfs diff=lfs merge=lfs -text +*.model filter=lfs diff=lfs merge=lfs -text +*.msgpack filter=lfs diff=lfs merge=lfs -text +*.onnx filter=lfs diff=lfs merge=lfs -text +*.ot filter=lfs diff=lfs merge=lfs -text +*.parquet filter=lfs diff=lfs merge=lfs -text +*.pb filter=lfs diff=lfs merge=lfs -text +*.pt filter=lfs diff=lfs merge=lfs -text +*.pth filter=lfs diff=lfs merge=lfs -text +*.rar filter=lfs diff=lfs merge=lfs -text +saved_model/**/* filter=lfs diff=lfs merge=lfs -text +*.tar.* filter=lfs diff=lfs merge=lfs -text +*.tflite filter=lfs diff=lfs merge=lfs -text +*.tgz filter=lfs diff=lfs merge=lfs -text +*.xz filter=lfs diff=lfs merge=lfs -text +*.zip filter=lfs diff=lfs merge=lfs -text +*.zstandard filter=lfs diff=lfs merge=lfs -text +*.tfevents* filter=lfs diff=lfs merge=lfs -text +*.db* filter=lfs diff=lfs merge=lfs -text +*.ark* filter=lfs diff=lfs merge=lfs -text +**/*ckpt*data* filter=lfs diff=lfs merge=lfs -text +**/*ckpt*.meta filter=lfs diff=lfs merge=lfs -text +**/*ckpt*.index filter=lfs diff=lfs merge=lfs -text +*.safetensors filter=lfs diff=lfs merge=lfs -text +*.ckpt filter=lfs diff=lfs merge=lfs -text +*.gguf* filter=lfs diff=lfs merge=lfs -text +*.ggml filter=lfs diff=lfs merge=lfs -text +*.llamafile* filter=lfs diff=lfs merge=lfs -text +*.pt2 filter=lfs diff=lfs merge=lfs -text +*.mlmodel filter=lfs diff=lfs merge=lfs -text +*.npy filter=lfs diff=lfs merge=lfs -text +*.npz filter=lfs diff=lfs merge=lfs -text +*.pickle filter=lfs diff=lfs merge=lfs -text +*.pkl filter=lfs diff=lfs merge=lfs -text +*.tar filter=lfs diff=lfs merge=lfs -text +*.wasm filter=lfs diff=lfs merge=lfs -text +*.zst filter=lfs diff=lfs merge=lfs -text +*tfevents* filter=lfs diff=lfs merge=lfs -text + +tokenizer_config.json filter=lfs diff=lfs merge=lfs -text +tokenizer.json filter=lfs diff=lfs merge=lfs -text \ No newline at end of file diff --git a/README.md b/README.md new file mode 100644 index 0000000..babb2c7 --- /dev/null +++ b/README.md @@ -0,0 +1,317 @@ +--- +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" {text}" + prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False) + + # Construct full sequence: [HUMAN] 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 (``, ``, ``, ``) + * **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. \ No newline at end of file diff --git a/chat_template.jinja b/chat_template.jinja new file mode 100644 index 0000000..1bad6a0 --- /dev/null +++ b/chat_template.jinja @@ -0,0 +1,93 @@ +{{- 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 %} diff --git a/config.json b/config.json new file mode 100644 index 0000000..f2b8330 --- /dev/null +++ b/config.json @@ -0,0 +1,35 @@ +{ + "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", + 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