Orpheus (hi, hi/en)

.gitattributes

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hi,en/Orpheus-tts-hi/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"
+---
+
+# Text to Speech for Indian Languages
+
+It  is a state-of-the-art neural text-to-speech (TTS) model specifically designed for Indian languages. Built on a Llama architecture backbone, It generates natural, expressive speech in Hindi and English with remarkable quality and ultra-low latency.
+
+## Model Overview
+
+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:** [email protected], [email protected]
+* **Model URL:** [https://huggingface.co/SachinTelecmi/Orpheus-tts-hi/tree/main](https://huggingface.co/SachinTelecmi/Orpheus-tts-hi/tree/main)
+
+## Key Features
+
+* **Multilingual Support:** Native Hindi and English capabilities with code-mixed support.
+* **Ultra-Fast Inference:** Sub-200ms latency on A100-80GB 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 this model, 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  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(
+    "SachinTelecmi/Orpheus-tts-hi",
+    quantization_config=quantization_config,
+    device_map="auto",
+    trust_remote_code=True,
+)
+tokenizer = AutoTokenizer.from_pretrained("SachinTelecmi/Orpheus-tts-hi", trust_remote_code=True)
+
+# Initialize SNAC decoder
+snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()
+
+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 = None 
+
+def generate_speech(text, speaker=None, 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)
+
+    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
+
+    # 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_model.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 ---
+
+# code-mixed
+prompt ='''Delhi की एक retail chain ने हमारे solutions से अपनी sales में 30% तक वृद्धि देखी है। <hmm..> उनका feedback बहुत encouraging रहा है ।'''
+audio = generate_speech(prompt)
+sf.write("output_1.wav", audio, 24000)
+
+prompt = '''जी हाँ, हमारे pricing plans काफी flexible हैं <breath> 
+    आप pay as you go या fixed subscription में से choose कर सकते हैं, ''' 
+audio = generate_speech(prompt)
+sf.write("output_2.wav", audio, 24000)
+
+```
+
+#### Streaming Inference Example
+1. Clone this repo
+   ```bash
+   git clone https://github.com/telecmi/Orpheus-TTS
+   ```
+2. Navigate and install packages
+   ```bash
+   cd Orpheus-TTS && pip install orpheus-speech # uses vllm under the hood for fast inference
+   ```
+   vllm pushed a slightly buggy version on March 18th so some bugs are being resolved by reverting to `pip install vllm==0.7.3` after `pip install orpheus-speech`
+
+   
+4. Run the example below:
+   ```python
+   from orpheus_tts import OrpheusModel
+   import wave
+   import time
+   
+   ## checkpoints folder form huggingface 
+   ## https://huggingface.co/SachinTelecmi/Orpheus-tts-hi
+   
+   model = OrpheusModel(model_name ="checkpoints", max_model_len=2048)
+   prompt ='''Delhi की एक retail chain ने हमारे solutions से अपनी sales में 30% तक वृद्धि देखी है। <hmm..> उनका feedback बहुत encouraging रहा है ।'''
+   filename = "prompt-hi.wav"
+   start_time = time.monotonic()
+   syn_tokens = model.generate_speech(
+      prompt=prompt,
+      voice=None,
+      )
+
+   with wave.open(filename, "wb") as wf:
+      wf.setnchannels(1)
+      wf.setsampwidth(2)
+      wf.setframerate(24000)
+
+      total_frames = 0
+      chunk_counter = 0
+      for audio_chunk in syn_tokens:  # output streaming
+         chunk_counter += 1
+         frame_count = len(audio_chunk) // (wf.getsampwidth() * wf.getnchannels())
+         total_frames += frame_count
+         wf.writeframes(audio_chunk)
+      duration = total_frames / wf.getframerate()
+
+   end_time = time.monotonic()
+   print(f"It took {end_time - start_time} seconds to generate {duration:.2f} seconds of audio")
+   Audio(filename)
+
+   # inference script is in Orpheus-TTS/realtime_streaming_example/streaming.py
+
+   ```
+### Samples
+[▶️ Listen: Output_1.wav](https://huggingface.co/SachinTelecmi/Orpheus-tts-hi/resolve/main/audio/output_1.wav) <br>
+
+[▶️ Listen: Output_2.wav](https://huggingface.co/SachinTelecmi/Orpheus-tts-hi/resolve/main/audio/output_2.wav)
+
+# Voice Cloning Example 
+```python 
+# check https://github.com/telecmi/Orpheus-TTS/tree/main/voice_clone/clone.py script 
+
+from voice_clone import OrpheusTTSVoiceClone
+from pathlib import Path
+
+voice_cloner = OrpheusTTSVoiceClone(model_name = "SachinTelecmi/Orpheus-tts-hi",device="cuda")
+    
+# Text to synthesize
+target_texts = [
+   "Hi IIT madras is currently doing great for indian research and its proud to be associated with it."
+]
+
+reference_pairs = [(".voice_clone/input_reference.wav", 
+                  "Delhi की एक retail chain ने हमारे solutions से अपनी sales में 30% तक वृद्धि देखी है। <hmm..> उनका feedback बहुत encouraging रहा है ।")]
+# Process each reference
+for audio_path, transcript in reference_pairs:
+   print(f"Processing reference: {audio_path} - {transcript}")
+   
+   # Clone voice
+   cloned_audio = voice_cloner.clone_voice(audio_path, transcript, target_texts)
+   
+   # Prepare output paths
+   audio_stem = Path(audio_path).stem
+   output_dir = Path(audio_path).parent / "inference"
+   output_paths = [
+      str(output_dir / f"{audio_stem}_{i}.wav") 
+      for i in range(len(target_texts))
+   ]
+   
+   # Save cloned audio
+   voice_cloner.save_audio(cloned_audio, output_paths)
+
+```
+
+
+
+## Uses
+
+It 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.
+  * **Real time streaming** Supports real time streaming with vllm TTFB around < 200ms on a100
+
+## Technical Improvements
+ * To get the ultralow latency, the fundamental solution would be warming up the SNAC decoder which is the bottleneck here!! while implementing vllm to improve the latency we have to warm up each and every module to get the better performance.
+
+### Architecture
+
+It 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 Non Speech tokens (`<hmm..>`, `<breath>`, `<think>` etc..)
+  * **Parameter-Efficient Training:** LoRA adaptation with differentiated ranks for attention and FFN modules.
+  * **Context Length:** 4096 tokens
+
+### Training
+
+#### Training Infrastructure
+
+  * **Hardware:** 1× NVIDIA A100 80GB GPUs
+  * **Precision:** BF16 mixed precision training with gradient checkpointing using Unsloth library 
+  * **Memory Optimization:** 4-bit quantization
+
+#### Training Configuration
+    Full fine tuning 
+
+#### Training Data
+
+It was trained on **proprietary, high-quality datasets** specifically curated for Indian language TTS.
+
+  * **Data Volume:** 4000 audio utterances of a single speaker
+  * **Languages:** Native Hindi and English utterances with code-mixed support
+  * **Speaker Diversity:** 1 professional voice artist 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.
+                       |
+
+## Risks, Limitations and Biases
+
+  * **Language Support:** Currently supports only Hindi and English. Performance on other Indian languages is not guaranteed.
+  * **Speaker Diversity:** Limited to 1 speaker voice, 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
+
+actively working on expanding its capabilities:
+
+  * Support for Odia, Tamil, Telugu, Bengali, Marathi, and other Indian languages.
+  * Additional speaker voices with regional accents.
+  * Emotion and prosody control tokens.
+  * CPU optimization for edge deployment.
+  * Served with TensorRT-LLM engine (On-going)
+
+## Acknowledgments
+This project builds on [unsloth/orpheus-3b-0.1-ft](https://huggingface.co/unsloth/orpheus-3b-0.1-ft) by Unsloth.

hi,en/Orpheus-tts-hi/audio/output_1.wav

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hi,en/Orpheus-tts-hi/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 %}

hi,en/Orpheus-tts-hi/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,
+    "pad_token_id": 128004,
+    "pretraining_tp": 1,
+    "rms_norm_eps": 1e-05,
+    "rope_scaling": {
+        "factor": 32.0,
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hi,en/Orpheus-tts-hi/source.txt

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+https://huggingface.co/SachinTelecmi/Orpheus-tts-hi

hi,en/Orpheus-tts-hi/special_tokens_map.json

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+{
+  "additional_special_tokens": [
+    "<|audio|>"
+  ],
+  "bos_token": {
+    "content": "<|begin_of_text|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "<|eot_id|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": "<custom_token_7>"
+}

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hi,en/new_orpheus/README.md

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+---
+license: llama3.2
+base_model: canopylabs/3b-hi-pretrain-research_release
+tags:
+- text-to-speech
+- hindi
+- hinglish
+- audio-generation
+- fine-tuned
+- unsloth
+language:
+- hi
+- en
+pipeline_tag: text-generation
+---
+
+# Hinglish TTS 3B Model
+
+This is a fine-tuned version of [canopylabs/3b-hi-pretrain-research_release](https://huggingface.co/canopylabs/3b-hi-pretrain-research_release) specialized for Hinglish (Hindi-English mixed) text-to-speech generation.
+
+## Model Details
+
+- **Base Model**: canopylabs/3b-hi-pretrain-research_release
+- **Fine-tuning Method**: LoRA with Unsloth (merged)
+- **Languages**: Hindi, English, Hinglish
+- **Task**: Text-to-Speech via audio token generation
+- **Model Size**: ~3B parameters
+
+## Usage
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
+
+# Load model and tokenizer
+model_name = "Itsharshi/new_orpheus"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype=torch.float16,
+    device_map="auto"
+)
+
+# Generate text
+prompt = "Hello doston, main aapka dost hun"
+inputs = tokenizer(prompt, return_tensors="pt")
+outputs = model.generate(**inputs, max_new_tokens=1200)
+```
+
+## Fine-tuning Details
+
+- **LoRA Rank**: 64
+- **LoRA Alpha**: 64
+- **Target Modules**: q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
+- **Training Framework**: Unsloth
+
+## Audio Generation
+
+This model generates audio tokens that need to be decoded using a SNAC (Scalable Neural Audio Codec) model:
+
+```python
+from snac import SNAC
+
+# Load SNAC decoder
+snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz")
+
+# Process generated tokens to audio codes and decode
+# (See full implementation in the original training code)
+```
+
+## Limitations
+
+- Requires SNAC model for audio generation
+- Optimized for Hinglish content
+- May not perform well on pure English or pure Hindi in some cases
+
+## Citation
+
+If you use this model, please cite the original base model:
+
+```bibtex
+@misc{canopylabs-3b-hi,
+  title={3B Hindi Pretrained Model},
+  author={Canopy Labs},
+  year={2024},
+  url={https://huggingface.co/canopylabs/3b-hi-pretrain-research_release}
+}
+```

hi,en/new_orpheus/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 %}

hi,en/new_orpheus/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",
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+  "num_hidden_layers": 28,
+  "num_key_value_heads": 8,
+  "pad_token_id": 128004,
+  "pretraining_tp": 1,
+  "rms_norm_eps": 1e-05,
+  "rope_scaling": {
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+    "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": "4.55.2",
+  "unsloth_version": "2025.8.6",
+  "use_cache": true,
+  "vocab_size": 156940
+}

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+  "pad_token_id": 128004,
+  "temperature": 0.6,
+  "top_p": 0.9,
+  "transformers_version": "4.55.2"
+}

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+---
+language:
+- hi
+tags:
+- text-to-speech
+- tts
+- audio
+- speech-synthesis
+- orpheus
+- gguf
+license: apache-2.0
+datasets:
+- internal
+---
+
+# Orpheus-3b-FT-Q8_0
+
+This is a quantised version of [canopylabs/3b-hi-ft-research_release](https://huggingface.co/canopylabs/3b-hi-ft-research_release).
+
+Orpheus is a high-performance Text-to-Speech model fine-tuned for natural, emotional speech synthesis. This repository hosts the 8-bit quantised version of the 3B parameter model, optimised for efficiency while maintaining high-quality output.
+
+## Model Description
+
+**Orpheus-3b-FT-Q8_0** is a 3 billion parameter Text-to-Speech model that converts text inputs into natural-sounding speech with support for multiple voices and emotional expressions. The model has been quantised to 8-bit (Q8_0) format for efficient inference, making it accessible on consumer hardware.
+
+Key features:
+- 1 distinct voice option with different characteristics
+- Support for emotion tags like laughter, sighs, etc.
+- Optimised for CUDA acceleration on RTX GPUs
+- Produces high-quality 24kHz mono audio
+- Fine-tuned for conversational naturalness
+
+## How to Use
+
+This model is designed to be used with an LLM inference server that connects to the [Orpheus-FastAPI](https://github.com/Lex-au/Orpheus-FastAPI) frontend, which provides both a web UI and OpenAI-compatible API endpoints.
+
+### Compatible Inference Servers
+
+This quantised model can be loaded into any of these LLM inference servers:
+
+- [GPUStack](https://github.com/gpustack/gpustack) - GPU optimised LLM inference server (My pick) - supports LAN/WAN tensor split parallelisation
+- [LM Studio](https://lmstudio.ai/) - Load the GGUF model and start the local server
+- [llama.cpp server](https://github.com/ggerganov/llama.cpp) - Run with the appropriate model parameters
+- Any compatible OpenAI API-compatible server
+
+### Quick Start
+
+1. Download this quantised model from [lex-au's Orpheus-FASTAPI collection](https://huggingface.co/collections/lex-au/orpheus-fastapi-67e125ae03fc96dae0517707)
+
+2. Load the model in your preferred inference server and start the server.
+
+3. Clone the Orpheus-FastAPI repository:
+```bash
+git clone https://github.com/Lex-au/Orpheus-FastAPI.git
+cd Orpheus-FastAPI
+```
+
+4. Configure the FastAPI server to connect to your inference server by setting the `ORPHEUS_API_URL` environment variable.
+
+5. Follow the complete installation and setup instructions in the [repository README](https://github.com/Lex-au/Orpheus-FastAPI).
+
+### Available Voices
+
+The model supports 1 voice:
+- `ऋतिका`: Female, Hindi, expressive
+
+### Emotion Tags
+
+You can add expressiveness to speech by inserting tags:
+- `<laugh>`, `<chuckle>`: For laughter sounds
+- `<sigh>`: For sighing sounds
+- `<cough>`, `<sniffle>`: For subtle interruptions
+- `<groan>`, `<yawn>`, `<gasp>`: For additional emotional expression
+
+## Technical Specifications
+
+- **Architecture**: Specialised token-to-audio sequence model
+- **Parameters**: ~3 billion
+- **Quantisation**: 8-bit (GGUF Q8_0 format)
+- **Audio Sample Rate**: 24kHz
+- **Input**: Text with optional voice selection and emotion tags
+- **Output**: High-quality WAV audio
+- **Language**: Hindi
+- **Hardware Requirements**: CUDA-compatible GPU (recommended: RTX series)
+- **Integration Method**: External LLM inference server + Orpheus-FastAPI frontend
+
+## Limitations
+
+- Currently supports English text only
+- Best performance achieved on CUDA-compatible GPUs
+- Generation speed depends on GPU capability
+
+## License
+
+This model is available under the [Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0).
+
+## Citation & Attribution
+
+The original Orpheus model was created by Canopy Labs. This repository contains a quantised version optimised for use with the Orpheus-FastAPI server.
+
+If you use this quantised model in your research or applications, please cite:
+
+```
+@misc{orpheus-tts-2025,
+  author = {Canopy Labs},
+  title = {Orpheus-3b-0.1-ft: Text-to-Speech Model},
+  year = {2025},
+  publisher = {HuggingFace},
+  howpublished = {\url{https://huggingface.co/canopylabs/orpheus-3b-0.1-ft}}
+}
+
+@misc{orpheus-quantised-2025,
+  author = {Lex-au},
+  title = {Orpheus-3b-FT-Q8_0: Quantised TTS Model with FastAPI Server},
+  note = {GGUF quantisation of canopylabs/orpheus-3b-0.1-ft},
+  year = {2025},
+  publisher = {HuggingFace},
+  howpublished = {\url{https://huggingface.co/lex-au/Orpheus-3b-FT-Q8_0.gguf}}
+}
+```

hi/Orpheus-3b-Hindi-FT-Q8_0.gguf/source.txt

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+https://huggingface.co/lex-au/Orpheus-3b-Hindi-FT-Q8_0.gguf