orpheus-3b-0.1-ft_gguf

orpheus-3b-0.1-ft_gguf

gguf version of canopylabs/orpheus-3b-0.1-ft that is English TTS model.

Evaluation

Accuracy assessment

I used the LJ-Speech-Dataset for evaluation. This public domain speech dataset consists of 13,100 short audio clips of a single speaker reading passages from 7 non-fiction books.

Evaluation process:

1. For each quantized model, 1000 randomly selected texts were synthesized into speech (though some models failed to vocalize certain samples)
2. Transcribed the speech using openai/whisper-large-v3-turbo
3. Measured WER (Word Error Rate) and CER (Character Error Rate)
4. For comparison, also transcribed the original human voice from the dataset to compare error rates

The llama-server was launched with the following command:

llama-server -m orpheus-3b-Q4_K_L.gguf --prio 3 -c 2048 -n -2 -fa -ngl 99 --no-webui 

Temperature and other parameters were left at their default values. Unfortunately, I haven't yet been able to identify optimal parameters. With optimal parameters, results could potentially improve further.

Evaluation Results

The results for each quantization level are as follows. Each model was tested with 1000 samples, but some models failed to vocalize certain samples. For models with fewer than 1000 evaluation samples, the difference represents the number of failed samples("Failed" column in the table below).

Raw Result

bench_result.zip

Performance Analysis

While the differences between quantization levels might not seem significant at first glance, there is a trend where lower bit quantization leads to increased pronunciation failures.
And f16 variant (--output-tensor-type f16 --token-embedding-type f16) appears to suppress regeneration failure. This could potentially be improved in the future with better quantization techniques or domain-specific finetuning.

Quality assessment

Audio quality assessment using facebookresearch/audiobox-aesthetics

Production Quality (PQ): The technical quality of the audio (clarity, fidelity, dynamics)
Content Enjoyment (CE): How well the listener enjoys the audio

Analysis Results

• Most Stable Model: q4_k_f16 (Lowest CE standard deviation)
• Highest PQ Score: q4_k_l and q4_k_f16 (7.26)
• Highest CE Score: q6_f16 and q4_k_f16 (5.93)
• Best Balance: q4_k_f16 (High scores in both metrics with low standard deviation)

Raw Result

facebookresearch_audiobox_aesthetics.ipynb

Speed assessment (bonus)

CPU Test environment: AMD Ryzen 9 7940HS w/ Radeon 780M Graphics 4.00 GHz
The following are speed test results using the Q4_K_L model:

CPU (Without Vulkan)

Speed of the first sample:

• TTFB (Time To First Byte, time until the first response): 356.19ms
• Processing speed: 8.09 tokens/second

CPU (With Vulkan)

Sample processing speed significantly improved:

• TTFB: 281.52ms
• Processing speed: approximately 16 tokens/second
• About 2x speed improvement compared to without Vulkan

GPU (RTX 4060)

Even faster processing:

• TTFB: 233.04ms
• Processing speed: approximately 73 tokens/second
• About 4x faster than CPU (with Vulkan) and over 9x faster than CPU (without Vulkan)

Conclusion

Our experiments show that quantization level has a relatively small impact on sound quality. However, low-bit quantization tends to increase pronunciation errors.

Processing speed varies significantly based on the execution environment. GPU execution provides the best performance for real-time conversation applications.
Research shows that for English, humans expect a response between -280 ms and +758 ms from the end of the utterance.
The real-world pipeline (VAD -> EOU -> ASR -> LLM -> TTS) is more complex, but we believe that Local LLM is approaching the point where natural voice conversations become possible.

How to use.

This model is designed to closely mimic the behavior of the original model.
Please note that this model is optimized with a original prompt template pattern.
Using it with applications designed for other quantized versions may lead to unexpected behavior or reduced performance.

To get started, you can use the sample script provided below.

sample script

sample llama.cpp server command.

llama-server -m orpheus-3b-Q4_K_L.gguf --prio 3 -c 2048 -n -2 --port 8080 --host 127.0.0.1 --no-webui

sample realtime play sample script.

import asyncio
import json
import sys
import httpx
import re
import torch
import scipy.io.wavfile as wavfile
from snac import SNAC
import locale
import argparse
from typing import List, Optional, Dict, Any, Generator
import numpy as np
import pyaudio
import time
import os
from io import BytesIO

# Set UTF-8 as the default encoding
locale.getpreferredencoding = lambda: "UTF-8"

class LlamaAudioConverter:
    def __init__(self, server_url="http://127.0.0.1:8080", output_file="output.wav", 
                 verbose=False, realtime_playback=True):
        """
        A class to handle audio conversion from Llama token stream
        
        Args:
            server_url: URL of the Llama server
            output_file: Path to save the output WAV file
            verbose: Enable detailed logging
            realtime_playback: Enable real-time audio playback
        """
        self.server_url = server_url
        self.output_file = output_file
        self.verbose = verbose
        self.collected_tokens = []
        self.realtime_playback = realtime_playback
        self.audio_chunks = []
        self.sample_rate = 24000
        
        # Performance measurement variables
        self.start_time = None
        self.ttfb = None
        self.audio_start_time = None
        self.token_count = 0
        
        # Initialize PyAudio (only for real-time playback)
        if self.realtime_playback:
            self.p = pyaudio.PyAudio()
            self.stream = self.p.open(
                format=pyaudio.paFloat32,
                channels=1,
                rate=self.sample_rate,
                output=True
            )
        else:
            self.p = None
            self.stream = None
        
        # Load SNAC model
        print("Loading SNAC model...")
        self.snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz")
        self.snac_model.to("cpu")  # Move to CPU for processing
    
    async def process_stream(self, response_stream):
        """Process the streaming response and collect tokens"""
        buffer = ""
        first_byte_received = False
        
        # Start stream processing
        async for chunk in response_stream:
            # Record time when first byte is received
            if not first_byte_received:
                self.ttfb = time.time() - self.start_time
                print(f"TTFB: {self.ttfb * 1000:.2f}ms")
                first_byte_received = True
            
            if chunk:
                try:
                    if isinstance(chunk, bytes):
                        chunk_text = chunk.decode('utf-8', errors='replace')
                    else:
                        chunk_text = chunk
                    
                    # Check data format (whether it's SSE format)
                    for line in chunk_text.strip().split('\n'):
                        if not line.strip():
                            continue
                        
                        # Check for SSE format
                        if line.startswith("data: "):
                            line = line[6:]  # Remove "data: " prefix
                        
                        data = json.loads(line)
                        if "content" in data and data["content"]:
                            content = data["content"]
                            buffer += content
                            
                            # Extract tokens
                            pattern = r'<custom_token_(\d+)>'
                            matches = re.findall(pattern, content)
                            
                            for match in matches:
                                token_id = 128256 + int(match)
                                self.collected_tokens.append(token_id)
                                self.token_count += 1
                                
                                if self.verbose:
                                    print(f"Token detected: <custom_token_{int(match)}> -> ID: {token_id}")
                            
                            # Process in chunks of 7 tokens only in real-time mode
                            if self.realtime_playback and len(self.collected_tokens) >= 7 and len(self.collected_tokens) % 7 == 0:
                                await self.process_collected_tokens()
                                
                                # Record time of first audio chunk generation
                                if self.audio_start_time is None and self.audio_chunks:
                                    self.audio_start_time = time.time()
                                    print(f"Time to first audio chunk: {(self.audio_start_time - self.start_time) * 1000:.2f}ms")
                                
                except json.JSONDecodeError as e:
                    if self.verbose:
                        print(f"Warning: Invalid JSON: {chunk_text}, Error: {e}")
                except Exception as e:
                    if self.verbose:
                        print(f"Error during chunk processing: {str(e)}")
        
        # Process all tokens after receiving the complete stream
        if not self.realtime_playback:
            # In no-playback mode, process all tokens at once
            print(f"Received all tokens. Generating audio in batch...")
            await self.process_all_tokens()
        else:
            # Process remaining tokens (for real-time playback mode)
            if self.collected_tokens:
                await self.process_collected_tokens(final=True)
            
        # Display performance metrics
        total_time = time.time() - self.start_time
        print(f"\nProcessing completed:")
        print(f"Total processing time: {total_time:.2f} seconds")
        print(f"TTFB: {self.ttfb * 1000:.2f}ms")
        if self.audio_start_time:
            print(f"Time to first audio chunk: {(self.audio_start_time - self.start_time) * 1000:.2f}ms")
        print(f"Tokens processed: {self.token_count}")
        print(f"Tokens per second: {self.token_count / total_time:.2f}")
            
        # Close the audio stream (only for real-time mode)
        if self.realtime_playback and self.stream:
            self.stream.stop_stream()
            self.stream.close()
            self.p.terminate()
            
        # Save response to file (for debugging)
        try:
            with open("response.txt", 'w', encoding='utf-8') as f:
                f.write(buffer)
            print(f"\nResponse saved to response.txt.")
        except Exception as e:
            print(f"Failed to write to file: {e}")
            
        # Combine and save all audio chunks
        if self.audio_chunks and self.output_file:
            try:
                # Combine audio chunks
                combined_audio = np.concatenate(self.audio_chunks)
                
                # Save as WAV file
                print(f"Saving audio to WAV file: {self.output_file}")
                wavfile.write(self.output_file, self.sample_rate, combined_audio)
            except Exception as e:
                print(f"Error while saving audio file: {e}")
    
    async def process_all_tokens(self):
        """Process all tokens at once to generate audio (for non-real-time mode)"""
        if not self.collected_tokens:
            return
            
        # Adjust to a multiple of 7
        tokens_count = (len(self.collected_tokens) // 7) * 7
        if tokens_count == 0:
            print("Warning: No tokens in multiples of 7. No audio will be generated.")
            return
            
        tokens_to_process = self.collected_tokens[:tokens_count]
        
        try:
            print(f"Batch processing {len(tokens_to_process)} tokens...")
            
            # Redistribute codes
            codes = self.redistribute_codes(tokens_to_process)
            
            # Generate audio
            with torch.inference_mode():
                audio_hat = self.snac_model.decode(codes)
            
            # Convert to NumPy array
            audio_np = audio_hat.detach().squeeze().to("cpu").numpy()
            
            # Save audio chunk
            self.audio_chunks.append(audio_np)
            
            # Record time of audio generation
            if self.audio_start_time is None:
                self.audio_start_time = time.time()
                print(f"Time to audio generation completion: {(self.audio_start_time - self.start_time) * 1000:.2f}ms")
            
        except Exception as e:
            print(f"Error during audio processing: {e}")
    
    async def process_collected_tokens(self, final=False):
        """Process collected tokens to convert to audio (for real-time mode)"""
        if not self.collected_tokens or not self.realtime_playback:
            return
            
        # Determine how many tokens to process
        if final:
            # Final processing - adjust to a multiple of 7
            remaining = len(self.collected_tokens) % 7
            if remaining > 0:
                # Truncate to a multiple of 7
                tokens_to_process = self.collected_tokens[:-remaining] if remaining > 0 else self.collected_tokens
            else:
                tokens_to_process = self.collected_tokens
            self.collected_tokens = []
        else:
            # Process tokens in multiples of 7 (appropriate size for audio chunks)
            tokens_count = (len(self.collected_tokens) // 7) * 7
            tokens_to_process = self.collected_tokens[:tokens_count]
            self.collected_tokens = self.collected_tokens[tokens_count:]
        
        if not tokens_to_process:
            return
            
        try:
            # Redistribute codes
            codes = self.redistribute_codes(tokens_to_process)
            
            # Generate audio
            with torch.inference_mode():
                audio_hat = self.snac_model.decode(codes)
            
            # Convert to NumPy array
            audio_np = audio_hat.detach().squeeze().to("cpu").numpy()
            
            # Save audio chunk
            self.audio_chunks.append(audio_np)
            
            # Real-time playback
            if self.stream:
                try:
                    # Play audio data as Float32
                    self.stream.write(audio_np.astype(np.float32).tobytes())
                    if self.verbose:
                        print(f"Playing audio chunk ({len(audio_np)} samples)...")
                except Exception as e:
                    print(f"Error during audio playback: {e}")
                    
            if self.verbose:
                print(f"Processed {len(tokens_to_process)} tokens. Remaining: {len(self.collected_tokens)}")
        
        except Exception as e:
            print(f"Error during audio processing: {e}")
    
    def redistribute_codes(self, tokens: List[int]) -> List[torch.Tensor]:
        """Redistribute token IDs to SNAC codes"""
        
        # Adjust to a multiple of 7
        code_length = (len(tokens) // 7) * 7
        if len(tokens) > code_length:
            tokens = tokens[:code_length]
            if self.verbose:
                print(f"Warning: Truncated token list to multiple of 7 ({code_length})")
        
        # Subtract 128266 from token IDs (128256 + custom value)
        code_list = [t - 128266 for t in tokens]
        
        layer_1 = []
        layer_2 = []
        layer_3 = []
        
        for i in range(len(code_list) // 7):
            layer_1.append(code_list[7*i])
            layer_2.append(code_list[7*i+1]-4096)
            layer_3.append(code_list[7*i+2]-(2*4096))
            layer_3.append(code_list[7*i+3]-(3*4096))
            layer_2.append(code_list[7*i+4]-(4*4096))
            layer_3.append(code_list[7*i+5]-(5*4096))
            layer_3.append(code_list[7*i+6]-(6*4096))
        
        codes = [
            torch.tensor(layer_1).unsqueeze(0),
            torch.tensor(layer_2).unsqueeze(0),
            torch.tensor(layer_3).unsqueeze(0)
        ]
        
        return codes
    
    async def send_prompt_to_llama(self, prompt: str) -> None:
        """Send prompt to the specified Llama server and process streaming response"""
        
        # Record start time
        self.start_time = time.time()
        self.token_count = 0
        self.audio_start_time = None
        
        # Request payload
        payload = {
            "prompt": prompt,
            "temperature": 0.8,
            "top_p": 0.95,
            "top_k": 40,
            "min_p": 0.05,
            "n_predict": 2048,
            "stream": True  # Enable streaming mode
        }
        
        print(f"Sending prompt to server {self.server_url}...")
        if self.verbose:
            print(f"Prompt: {prompt}")
        
        # Display operation mode information
        if self.realtime_playback:
            print("Real-time playback mode: Enabled (processing and playing tokens as they arrive)")
        else:
            print("Real-time playback mode: Disabled (batch processing all tokens after completion)")
        
        # Use async HTTP client
        async with httpx.AsyncClient(timeout=None) as client:
            try:
                # Streaming request
                async with client.stream(
                    "POST",
                    f"{self.server_url}/completion", 
                    json=payload,
                    headers={"Accept": "application/x-ndjson"}
                ) as response:
                    response.raise_for_status()
                    
                    # Process stream
                    await self.process_stream(response.aiter_bytes())
                    
            except httpx.HTTPError as e:
                print(f"HTTP Error: {e}")
            except Exception as e:
                print(f"Error: {e}")

async def main():
    parser = argparse.ArgumentParser(description='Send prompts to Llama server and generate/play audio')
    parser.add_argument('--text', '-t', default="Hello, My name is tara.", 
                        help='Text to synthesize into speech')
    parser.add_argument('--output', '-o', default='output.wav', 
                        help='Output WAV file path (default: output.wav)')
    parser.add_argument('--server', '-s', default='http://127.0.0.1:8080', 
                        help='Llama server address (default: http://127.0.0.1:8080)')
    parser.add_argument('--verbose', '-v', action='store_true', 
                        help='Enable verbose logging')
    parser.add_argument('--no-playback', '-np', action='store_true',
                        help='Disable real-time playback and process all tokens after completion')
    args = parser.parse_args()
    
    # Check dependencies (only if real-time playback is enabled)
    if not args.no_playback:
        try:
            import pyaudio
        except ImportError:
            print("Warning: PyAudio is not installed. Disabling real-time playback.")
            print("To install: pip install pyaudio")
            args.no_playback = True
    
    # Initialize converter
    converter = LlamaAudioConverter(
        server_url=args.server,
        output_file=args.output,
        verbose=args.verbose,
        realtime_playback=not args.no_playback
    )
    
    # Construct prompt
    prompt = f"<custom_token_3><|begin_of_text|>tara: {args.text}<|eot_id|><custom_token_4><custom_token_5><custom_token_1>"
    
    # Send prompt to server
    await converter.send_prompt_to_llama(prompt)

if __name__ == "__main__":
    asyncio.run(main())

For real-time playback

For real-time playback, you need hardware capable of processing at least 90 tokens per second.
Also, this sample does not improve the problem of noise being mixed in during real-time playback.

Many people have been trying it out with Orpheus-TTS github, so it's recommended to refer to this repository for additional information.

python script.py --text "Hello, how are you today?" --server http://your-server-address:8080

For batch processing (no playback)

python script.py --text "Hello, how are you today?" --server http://your-server-address:8080 --no-playback

See also

Reddit discussion

Acknowledgements

I would like to thank the contributors of canopylabs/orpheus-3b-0.1-ft, hubertsiuzdak/snac, meta/llama3, ggml-org/llama.cpp, openai/whisper-large-v3-turbo, facebookresearch/audiobox-aesthetics and LJ-Speech-Dataset.

Citation

If you find this work on TTS model quantization useful for your research or applications, please consider citing it as follows:

Developed by: [dahara1@webbigdata]
Language(s) (NLP): [English]
base model : canopylabs/orpheus-3b-0.1-ft
BibTeX:

@misc{dahara1_2025_orpheus_gguf,
  author       = {dahara1@webbigdata},
  title        = {Orpheus-3b GGUF Quantization Analysis},
  year         = {2025},
  howpublished = {\url{https://huggingface.co/dahara1/orpheus-3b-0.1-ft_gguf}},
  note         = {Accessed: 2025-05-08},
  abstract     = {Comprehensive analysis of different quantization levels for the Orpheus-3b TTS model, examining accuracy, quality, and performance metrics.},
}