Update .gitignore and benchmark scripts for GPU support; enhance TTS service handling and session management

.gitignore

@@ -68,3 +68,4 @@ examples/*.acc
 examples/*.ogg
 examples/speech.mp3
 examples/phoneme_examples/output/*.wav
+examples/assorted_checks/benchmarks/output_audio/*

CHANGELOG.md

@@ -2,6 +2,34 @@
 
 Notable changes to this project will be documented in this file.
 
+## [v0.1.4] - 2025-01-30
+### Added
+- Smart Chunking System:
+  - New text_processor with smart_split for improved sentence boundary detection
+  - Dynamically adjusts chunk sizes based on sentence structure, using phoneme/token information in an intial pass
+  - Should avoid ever going over the 510 limit per chunk, while preserving natural cadence
+- Web UI Added (To Be Replacing Gradio):
+  - Integrated streaming with tempfile generation
+  - Download links available in X-Download-Path header
+  - Configurable cleanup triggers for temp files
+- Debug Endpoints:
+  - /debug/threads for thread information and stack traces
+  - /debug/storage for temp file and output directory monitoring
+  - /debug/system for system resource information
+  - /debug/session_pools for ONNX/CUDA session status
+- Automated Model Management:
+  - Auto-download from releases page
+  - Included download scripts for manual installation
+  - Pre-packaged voice models in repository
+
+### Changed
+- Significant architectural improvements:
+  - Multi-model architecture support
+  - Enhanced concurrency handling
+  - Improved streaming header management
+  - Better resource/session pool management
+
+
 ## [v0.1.2] - 2025-01-23
 ### Structural Improvements
 - Models can be manually download and placed in api/src/models, or use included script

README.md

@@ -3,51 +3,58 @@
 </p>
 
 # <sub><sub>_`FastKoko`_ </sub></sub>
-[![Tests](https://img.shields.io/badge/tests-104%20passed-darkgreen)]()
+[![Tests](https://img.shields.io/badge/tests-100%20passed-darkgreen)]()
 [![Coverage](https://img.shields.io/badge/coverage-49%25-grey)]()
 [![Tested at Model Commit](https://img.shields.io/badge/last--tested--model--commit-a67f113-blue)](https://huggingface.co/hexgrad/Kokoro-82M/tree/c3b0d86e2a980e027ef71c28819ea02e351c2667) [![Try on Spaces](https://img.shields.io/badge/%F0%9F%A4%97%20Try%20on-Spaces-blue)](https://huggingface.co/spaces/Remsky/Kokoro-TTS-Zero)
 
-> Pre-release. Not fully tested
+> Support for Kokoro-82M v1.0 coming very soon!
 
 Dockerized FastAPI wrapper for [Kokoro-82M](https://huggingface.co/hexgrad/Kokoro-82M) text-to-speech model
 - OpenAI-compatible Speech endpoint, with inline voice combination, and mapped naming/models for strict systems
 - NVIDIA GPU accelerated or CPU inference (ONNX, Pytorch) 
 - very fast generation time
-  - 35x-100x+ real time speed via 4060Ti+
-  - 5x+ real time speed via M3 Pro CPU
-- streaming support w/ variable chunking to control latency, (new) improved concurrency
+  - ~35x-100x+ real time speed via 4060Ti+
+  - ~5x+ real time speed via M3 Pro CPU
+- streaming support & tempfile generation
 - phoneme based dev endpoints
 - (new) Integrated web UI on localhost:8880/web
+- (new) Debug endpoints for monitoring threads, storage, and session pools
 
-> [!Tip]
-> You can try the new beta version from the `v0.1.2-pre` branch now:
-<table>
-  <tr>
-    <td>
-      <img src="https://github.com/user-attachments/assets/440162eb-1918-4999-ab2b-e2730990efd0" width="100%" alt="Voice Analysis Comparison" style="border: 2px solid #333; padding: 5px;">
-    </td>
-    <td>
-      <ul>
-        <li>Integrated web UI (on localhost:8880/web)</li>
-        <li>Better concurrency handling, baked in models and voices</li>
-        <li>Voice name/model mappings to OAI standard</li>
-        <pre> # with:
-docker run -p 8880:8880 ghcr.io/remsky/kokoro-fastapi-cpu:latest # CPU
-docker run --gpus all -p 8880:8880 ghcr.io/remsky/kokoro-fastapi-gpu:latest # Nvidia GPU
-        </pre>
-      </ul>
-    </td>
-  </tr>
-</table>
 
-<details open>
-<summary>Quick Start</summary>
+## Get Started
 
-The service can be accessed through either the API endpoints or the Gradio web interface.
+<details >
+<summary>Quickest Start (docker run)</summary>
+
+
+Pre built images are available to run, with arm/multi-arch support, and baked in models
+Refer to the core/config.py file for a full list of variables which can be managed via the environment
+
+```bash
+
+docker run -p 8880:8880 ghcr.io/remsky/kokoro-fastapi-cpu:v0.1.4 # CPU, or:
+docker run --gpus all -p 8880:8880 ghcr.io/remsky/kokoro-fastapi-gpu:v0.1.4 #NVIDIA GPU
+```
+
+Once running, access:
+- API Documentation: http://localhost:8880/docs
+- Web Interface: http://localhost:8880/web
+
+<div align="center" style="display: flex; justify-content: center; gap: 20px;">
+  <img src="assets/docs-screenshot.png" width="48%" alt="API Documentation" style="border: 2px solid #333; padding: 10px;">
+  <img src="assets/webui-screenshot.png" width="48%" alt="Web UI Screenshot" style="border: 2px solid #333; padding: 10px;">
+</div>
+
+</details>
+
+
+<details>
+
+<summary>Quick Start (docker compose) </summary>
 
 1. Install prerequisites, and start the service using Docker Compose (Full setup including UI):
    - Install [Docker](https://www.docker.com/products/docker-desktop/)
-
+   - 
    - Clone the repository:
         ```bash
         git clone https://github.com/remsky/Kokoro-FastAPI.git
@@ -61,19 +68,17 @@ The service can be accessed through either the API endpoints or the Gradio web i
         # python ../scripts/download_model.py --type onnx # for CPU
         ```
 
+        ```bash
+        Or directly via UV
+        ./start-cpu.sh
+        ./start-gpu.sh 
+        ```
+
       Once started:
      - The API will be available at http://localhost:8880
      - The *Web UI* can be tested at http://localhost:8880/web
      - The Gradio UI (deprecating) can be accessed at http://localhost:7860
 
-    __Or__ running the API alone using Docker (model + voice packs baked in) (Most Recent):
-          
-    ```bash
-    docker run -p 8880:8880 ghcr.io/remsky/kokoro-fastapi-cpu:v0.1.2 # CPU 
-    docker run --gpus all -p 8880:8880 ghcr.io/remsky/kokoro-fastapi-gpu:v0.1.2 # Nvidia GPU
-    ```
-        
-        
 2. Run locally as an OpenAI-Compatible Speech Endpoint
     ```python
     from openai import OpenAI
@@ -91,13 +96,50 @@ The service can be accessed through either the API endpoints or the Gradio web i
         response.stream_to_file("output.mp3")
     
     ```
+</details>
+<summary>Direct Run (via uv) </summary>
 
-  <div align="center">
-    <div style="display: flex; justify-content: center; gap: 20px;">
-      <img src="assets/beta_web_ui.png" width="45%" alt="Beta Web UI" style="border: 2px solid #333; padding: 10px;">
-      <img src="ui/GradioScreenShot.png" width="45%" alt="Voice Analysis Comparison" style="border: 2px solid #333; padding: 10px;">
-    </div>
-  </div>
+1. Install prerequisites ():
+   - Install [astral-uv](https://docs.astral.sh/uv/)
+   - Clone the repository:
+        ```bash
+        git clone https://github.com/remsky/Kokoro-FastAPI.git
+        cd Kokoro-FastAPI
+
+        # if you are missing any models, run:
+        # python ../scripts/download_model.py --type pth  # for GPU
+        # python ../scripts/download_model.py --type onnx # for CPU
+        ```
+
+        Start directly via UV (with hot-reload)
+        ```bash
+        ./start-cpu.sh OR
+        ./start-gpu.sh 
+        ```
+
+      Once started:
+     - The API will be available at http://localhost:8880
+     - The *Web UI* can be tested at http://localhost:8880/web
+     - The Gradio UI (deprecating) can be accessed at http://localhost:7860
+
+2. Run locally as an OpenAI-Compatible Speech Endpoint
+    ```python
+    from openai import OpenAI
+    client = OpenAI(
+        base_url="http://localhost:8880/v1",
+        api_key="not-needed"
+        )
+
+    with client.audio.speech.with_streaming_response.create(
+        model="kokoro", 
+        voice="af_sky+af_bella", #single or multiple voicepack combo
+        input="Hello world!",
+        response_format="mp3"
+    ) as response:
+        response.stream_to_file("output.mp3")
+    
+    ```
+</details>
 
 ## Features 
 <details>
@@ -211,13 +253,12 @@ If you only want the API, just comment out everything in the docker-compose.yml
 
 Currently, voices created via the API are accessible here, but voice combination/creation has not yet been added
 
-Running the UI Docker Service
+Running the UI Docker Service [deprecating]
    - If you only want to run the Gradio web interface separately and connect it to an existing API service:
       ```bash
       docker run -p 7860:7860 \
         -e API_HOST=<api-hostname-or-ip> \
         -e API_PORT=8880 \
-        ghcr.io/remsky/kokoro-fastapi-ui:v0.1.0
       ```
 
      - Replace `<api-hostname-or-ip>` with:
@@ -236,7 +277,7 @@ environment:
 
 When running the Docker image directly:
 ```bash
-docker run -p 7860:7860 -e DISABLE_LOCAL_SAVING=true ghcr.io/remsky/kokoro-fastapi-ui:latest
+docker run -p 7860:7860 -e DISABLE_LOCAL_SAVING=true ghcr.io/remsky/kokoro-fastapi-ui:v0.1.4
 ```
 </details>
 
@@ -247,7 +288,7 @@ docker run -p 7860:7860 -e DISABLE_LOCAL_SAVING=true ghcr.io/remsky/kokoro-fasta
 # OpenAI-compatible streaming
 from openai import OpenAI
 client = OpenAI(
-    base_url="http://localhost:8880", api_key="not-needed")
+    base_url="http://localhost:8880/v1", api_key="not-needed")
 
 # Stream to file
 with client.audio.speech.with_streaming_response.create(
@@ -329,17 +370,17 @@ Benchmarking was performed on generation via the local API using text lengths up
 </p>
 
 Key Performance Metrics:
-- Realtime Speed: Ranges between 25-50x (generation time to output audio length)
+- Realtime Speed: Ranges between 35x-100x (generation time to output audio length)
 - Average Processing Rate: 137.67 tokens/second (cl100k_base)
 </details>
 <details>
 <summary>GPU Vs. CPU</summary>
 
 ```bash
-# GPU: Requires NVIDIA GPU with CUDA 12.1 support (~35x realtime speed)
+# GPU: Requires NVIDIA GPU with CUDA 12.1 support (~35x-100x realtime speed)
 docker compose up --build
 
-# CPU: ONNX optimized inference (~2.4x realtime speed)
+# CPU: ONNX optimized inference (~5x+ realtime speed on M3 Pro)
 docker compose -f docker-compose.cpu.yml up --build
 ```
 *Note: Overall speed may have reduced somewhat with the structural changes to accomodate streaming. Looking into it* 
@@ -359,36 +400,61 @@ Convert text to phonemes and/or generate audio directly from phonemes:
 ```python
 import requests
 
-# Convert text to phonemes
+def get_phonemes(text: str, language: str = "a"):
+    """Get phonemes and tokens for input text"""
     response = requests.post(
         "http://localhost:8880/dev/phonemize",
-    json={
-        "text": "Hello world!",
-        "language": "a"  # "a" for American English
-    }
+        json={"text": text, "language": language}  # "a" for American English
     )
+    response.raise_for_status()
     result = response.json()
-phonemes = result["phonemes"]  # Phoneme string e.g  ðɪs ɪz ˈoʊnli ɐ tˈɛst
-tokens = result["tokens"]      # Token IDs including start/end tokens 
+    return result["phonemes"], result["tokens"]
 
-# Generate audio from phonemes
+def generate_audio_from_phonemes(phonemes: str, voice: str = "af_bella"):
+    """Generate audio from phonemes"""
     response = requests.post(
         "http://localhost:8880/dev/generate_from_phonemes",
-    json={
-        "phonemes": phonemes,
-        "voice": "af_bella",
-        "speed": 1.0
-    }
+        json={"phonemes": phonemes, "voice": voice},
+        headers={"Accept": "audio/wav"}
     )
+    if response.status_code != 200:
+        print(f"Error: {response.text}")
+        return None
+    return response.content
 
-# Save WAV audio
+# Example usage
+text = "Hello world!"
+try:
+    # Convert text to phonemes
+    phonemes, tokens = get_phonemes(text)
+    print(f"Phonemes: {phonemes}")  # e.g. ðɪs ɪz ˈoʊnli ɐ tˈɛst
+    print(f"Tokens: {tokens}")      # Token IDs including start/end tokens
+
+    # Generate and save audio
+    if audio_bytes := generate_audio_from_phonemes(phonemes):
         with open("speech.wav", "wb") as f:
-    f.write(response.content)
+            f.write(audio_bytes)
+        print(f"Generated {len(audio_bytes)} bytes of audio")
+except Exception as e:
+    print(f"Error: {e}")
 ```
 
 See `examples/phoneme_examples/generate_phonemes.py` for a sample script.
 </details>
 
+<details>
+<summary>Debug Endpoints</summary>
+
+Monitor system state and resource usage with these endpoints:
+
+- `/debug/threads` - Get thread information and stack traces
+- `/debug/storage` - Monitor temp file and output directory usage
+- `/debug/system` - Get system information (CPU, memory, GPU)
+- `/debug/session_pools` - View ONNX session and CUDA stream status
+
+Useful for debugging resource exhaustion or performance issues.
+</details>
+
 ## Known Issues
 
 <details>

api/src/inference/session_pool.py

@@ -119,13 +119,17 @@ class BaseSessionPool:
             # Clean expired sessions
             self._cleanup_expired()
             
+            # TODO: Change session tracking to use unique IDs instead of model paths
+            # This would allow multiple instances of the same model
+            
             # Check if session exists and is valid
             if model_path in self._sessions:
                 session_info = self._sessions[model_path]
                 session_info.last_used = time.time()
                 return session_info.session
                 
-            # Check if we can create new session
+            # TODO: Modify session limit check to count instances per model path
+            # Rather than total sessions across all models
             if len(self._sessions) >= self._max_size:
                 raise RuntimeError(
                     f"Maximum number of sessions reached ({self._max_size}). "

api/src/services/tts_service.py

@@ -50,6 +50,10 @@ class TTSService:
             try:
                 # Handle stream finalization
                 if is_last:
+                    # Skip format conversion for raw audio mode
+                    if not output_format:
+                        return np.array([], dtype=np.float32)
+                    
                     return await AudioService.convert_audio(
                         np.array([0], dtype=np.float32),  # Dummy data for type checking
                         24000,
@@ -229,7 +233,7 @@ class TTSService:
         try:
             # Use streaming generator but collect all chunks
             async for chunk in self.generate_audio_stream(
-                text, voice, speed, output_format=None
+                text, voice, speed,  # Default to WAV for raw audio
             ):
                 if chunk is not None:
                     chunks.append(chunk)

examples/assorted_checks/benchmarks/benchmark_tts_rtf.py

@@ -68,7 +68,7 @@ def main():
     # Initialize system monitor
     monitor = SystemMonitor(interval=1.0)  # 1 second interval
     # Set prefix for output files (e.g. "gpu", "cpu", "onnx", etc.)
-    prefix = "cpu"
+    prefix = "gpu"
     # Generate token sizes
     if "gpu" in prefix:
         token_sizes = generate_token_sizes(

examples/assorted_checks/benchmarks/output_data/cpu_benchmark_stats_rtf.txt

@@ -1,23 +1,23 @@
 === Benchmark Statistics (with correct RTF) ===
 
-Total tokens processed: 1800
-Total audio generated (s): 568.53
-Total test duration (s): 306.02
-Average processing rate (tokens/s): 5.75
-Average RTF: 0.55
-Average Real Time Speed: 1.81
+Total tokens processed: 1500
+Total audio generated (s): 427.90
+Total test duration (s): 10.84
+Average processing rate (tokens/s): 133.35
+Average RTF: 0.02
+Average Real Time Speed: 41.67
 
 === Per-chunk Stats ===
 
-Average chunk size (tokens): 600.00
-Min chunk size (tokens): 300
-Max chunk size (tokens): 900
-Average processing time (s): 101.89
-Average output length (s): 189.51
+Average chunk size (tokens): 300.00
+Min chunk size (tokens): 100
+Max chunk size (tokens): 500
+Average processing time (s): 2.13
+Average output length (s): 85.58
 
 === Performance Ranges ===
 
-Processing rate range (tokens/s): 5.30 - 6.26
-RTF range: 0.51x - 0.59x
-Real Time Speed range: 1.69x - 1.96x
+Processing rate range (tokens/s): 102.04 - 159.74
+RTF range: 0.02x - 0.03x
+Real Time Speed range: 33.33x - 50.00x
 

examples/assorted_checks/benchmarks/output_data/gpu_benchmark_results_rtf.json

@@ -2,616 +2,240 @@
   "results": [
     {
       "tokens": 150,
-      "processing_time": 2.36,
-      "output_length": 45.9,
-      "rtf": 0.05,
-      "elapsed_time": 2.44626
+      "processing_time": 1.18,
+      "output_length": 43.7,
+      "rtf": 0.03,
+      "elapsed_time": 1.20302
     },
     {
       "tokens": 300,
-      "processing_time": 4.94,
-      "output_length": 96.425,
-      "rtf": 0.05,
-      "elapsed_time": 7.46073
+      "processing_time": 2.27,
+      "output_length": 86.75,
+      "rtf": 0.03,
+      "elapsed_time": 3.49958
     },
     {
       "tokens": 450,
-      "processing_time": 8.94,
-      "output_length": 143.1,
-      "rtf": 0.06,
-      "elapsed_time": 16.55036
+      "processing_time": 3.49,
+      "output_length": 125.9,
+      "rtf": 0.03,
+      "elapsed_time": 7.03862
     },
     {
       "tokens": 600,
-      "processing_time": 19.78,
-      "output_length": 188.675,
-      "rtf": 0.1,
-      "elapsed_time": 36.69352
+      "processing_time": 4.64,
+      "output_length": 169.325,
+      "rtf": 0.03,
+      "elapsed_time": 11.71062
     },
     {
       "tokens": 750,
-      "processing_time": 19.89,
-      "output_length": 236.7,
-      "rtf": 0.08,
-      "elapsed_time": 56.77695
+      "processing_time": 5.07,
+      "output_length": 212.3,
+      "rtf": 0.02,
+      "elapsed_time": 16.83186
     },
     {
       "tokens": 900,
-      "processing_time": 16.83,
-      "output_length": 283.425,
-      "rtf": 0.06,
-      "elapsed_time": 73.8079
+      "processing_time": 6.66,
+      "output_length": 258.0,
+      "rtf": 0.03,
+      "elapsed_time": 23.54135
     }
   ],
   "system_metrics": [
     {
-      "timestamp": "2025-01-06T00:43:20.888295",
-      "cpu_percent": 36.92,
-      "ram_percent": 68.6,
-      "ram_used_gb": 43.6395263671875,
-      "gpu_memory_used": 7022.0,
-      "relative_time": 0.09646010398864746
+      "timestamp": "2025-01-30T05:06:38.733338",
+      "cpu_percent": 0.0,
+      "ram_percent": 18.6,
+      "ram_used_gb": 5.284908294677734,
+      "gpu_memory_used": 1925.0,
+      "relative_time": 0.039948463439941406
     },
-    {
-      "timestamp": "2025-01-06T00:43:21.983741",
-      "cpu_percent": 22.29,
-      "ram_percent": 68.6,
-      "ram_used_gb": 43.642677307128906,
-      "gpu_memory_used": 7021.0,
-      "relative_time": 1.1906661987304688
-    },
-    {
-      "timestamp": "2025-01-06T00:43:23.078293",
-      "cpu_percent": 27.39,
-      "ram_percent": 68.6,
-      "ram_used_gb": 43.61421203613281,
-      "gpu_memory_used": 7190.0,
-      "relative_time": 2.264479160308838
-    },
-    {
-      "timestamp": "2025-01-06T00:43:24.151445",
-      "cpu_percent": 20.28,
-      "ram_percent": 68.6,
-      "ram_used_gb": 43.65406036376953,
-      "gpu_memory_used": 7193.0,
-      "relative_time": 3.349093198776245
-    },
-    {
-      "timestamp": "2025-01-06T00:43:25.237021",
-      "cpu_percent": 23.03,
-      "ram_percent": 68.6,
-      "ram_used_gb": 43.647274017333984,
-      "gpu_memory_used": 7191.0,
-      "relative_time": 4.413560628890991
-    },
-    {
-      "timestamp": "2025-01-06T00:43:26.300255",
-      "cpu_percent": 23.62,
-      "ram_percent": 68.6,
-      "ram_used_gb": 43.642295837402344,
-      "gpu_memory_used": 7185.0,
-      "relative_time": 5.484973430633545
-    },
-    {
-      "timestamp": "2025-01-06T00:43:27.377319",
-      "cpu_percent": 46.04,
-      "ram_percent": 68.7,
-      "ram_used_gb": 43.7291374206543,
-      "gpu_memory_used": 7178.0,
-      "relative_time": 6.658120632171631
-    },
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-      "timestamp": "2025-01-06T00:43:28.546053",
-      "cpu_percent": 29.79,
-      "ram_percent": 68.7,
-      "ram_used_gb": 43.73202133178711,
-      "gpu_memory_used": 7177.0,
-      "relative_time": 7.725939035415649
-    },
-    {
-      "timestamp": "2025-01-06T00:43:29.613327",
-      "cpu_percent": 18.19,
-      "ram_percent": 68.8,
-      "ram_used_gb": 43.791343688964844,
-      "gpu_memory_used": 7177.0,
-      "relative_time": 8.800285577774048
-    },
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-      "timestamp": "2025-01-06T00:43:30.689097",
-      "cpu_percent": 22.29,
-      "ram_percent": 68.9,
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-      "gpu_memory_used": 7176.0,
-      "relative_time": 9.899119853973389
-    },
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-      "timestamp": "2025-01-06T00:43:31.786443",
-      "cpu_percent": 32.59,
-      "ram_percent": 68.9,
-      "ram_used_gb": 43.834510803222656,
-      "gpu_memory_used": 7189.0,
-      "relative_time": 11.042734384536743
-    },
-    {
-      "timestamp": "2025-01-06T00:43:32.929720",
-      "cpu_percent": 42.48,
-      "ram_percent": 68.8,
-      "ram_used_gb": 43.77507019042969,
-      "gpu_memory_used": 7192.0,
-      "relative_time": 12.117269277572632
-    },
-    {
-      "timestamp": "2025-01-06T00:43:34.004481",
-      "cpu_percent": 26.33,
-      "ram_percent": 68.8,
-      "ram_used_gb": 43.77891159057617,
-      "gpu_memory_used": 7192.0,
-      "relative_time": 13.19830870628357
-    },
-    {
-      "timestamp": "2025-01-06T00:43:35.086024",
-      "cpu_percent": 26.53,
-      "ram_percent": 68.8,
-      "ram_used_gb": 43.77515411376953,
-      "gpu_memory_used": 7192.0,
-      "relative_time": 14.29457426071167
-    },
-    {
-      "timestamp": "2025-01-06T00:43:36.183496",
-      "cpu_percent": 40.33,
-      "ram_percent": 68.9,
-      "ram_used_gb": 43.81095886230469,
-      "gpu_memory_used": 7192.0,
-      "relative_time": 15.402768850326538
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-      "relative_time": 72.95793795585632
-    },
-    {
-      "timestamp": "2025-01-06T00:44:34.848852",
-      "cpu_percent": 48.36,
-      "ram_percent": 66.5,
-      "ram_used_gb": 42.3160514831543,
-      "gpu_memory_used": 5924.0,
-      "relative_time": 74.35109186172485
-    },
-    {
-      "timestamp": "2025-01-06T00:44:36.240235",
-      "cpu_percent": 58.06,
-      "ram_percent": 67.5,
-      "ram_used_gb": 42.95722198486328,
-      "gpu_memory_used": 5930.0,
-      "relative_time": 75.47581958770752
-    },
-    {
-      "timestamp": "2025-01-06T00:44:37.363208",
-      "cpu_percent": 46.82,
-      "ram_percent": 67.6,
-      "ram_used_gb": 42.97764587402344,
-      "gpu_memory_used": 6364.0,
-      "relative_time": 76.58708119392395
-    },
+      "timestamp": "2025-01-30T05:07:01.747500",
+      "cpu_percent": 13.79,
+      "ram_percent": 18.9,
+      "ram_used_gb": 5.367362976074219,
+      "gpu_memory_used": 3620.0,
+      "relative_time": 23.05113124847412
+    },
     {
-      "timestamp": "2025-01-06T00:44:38.474408",
-      "cpu_percent": 50.93,
-      "ram_percent": 67.9,
-      "ram_used_gb": 43.1597900390625,
-      "gpu_memory_used": 6426.0,
-      "relative_time": 77.6842532157898
+      "timestamp": "2025-01-30T05:07:02.784828",
+      "cpu_percent": 10.16,
+      "ram_percent": 19.1,
+      "ram_used_gb": 5.443946838378906,
+      "gpu_memory_used": 1916.0,
+      "relative_time": 24.08937978744507
     }
   ],
-  "test_duration": 82.49591493606567
+  "test_duration": 26.596059799194336
 }

examples/assorted_checks/benchmarks/output_data/gpu_benchmark_stats_rtf.txt

@@ -1,23 +1,23 @@
 === Benchmark Statistics (with correct RTF) ===
 
 Total tokens processed: 3150
-Total audio generated (s): 994.22
-Total test duration (s): 73.81
-Average processing rate (tokens/s): 49.36
-Average RTF: 0.07
-Average Real Time Speed: 15.00
+Total audio generated (s): 895.98
+Total test duration (s): 23.54
+Average processing rate (tokens/s): 133.43
+Average RTF: 0.03
+Average Real Time Speed: 35.29
 
 === Per-chunk Stats ===
 
 Average chunk size (tokens): 525.00
 Min chunk size (tokens): 150
 Max chunk size (tokens): 900
-Average processing time (s): 12.12
-Average output length (s): 165.70
+Average processing time (s): 3.88
+Average output length (s): 149.33
 
 === Performance Ranges ===
 
-Processing rate range (tokens/s): 30.33 - 63.56
-RTF range: 0.05x - 0.10x
-Real Time Speed range: 10.00x - 20.00x
+Processing rate range (tokens/s): 127.12 - 147.93
+RTF range: 0.02x - 0.03x
+Real Time Speed range: 33.33x - 50.00x