Add comprehensive coverage of non-transformers ASR implementations

chapters/en/_toctree.yml

@@ -73,6 +73,8 @@
     title: What you'll learn and what you'll build
   - local: chapter5/asr_models
     title: Pre-trained models for speech recognition
+  - local: chapter5/alternative_implementations
+    title: Alternative ASR implementations
   - local: chapter5/choosing_dataset
     title: Choosing a dataset
   - local: chapter5/evaluation
@@ -85,7 +87,7 @@
     title: Hands-on exercise
   - local: chapter5/supplemental_reading
     title: Supplemental reading and resources
-#
+
 - title: Unit 6. From text to speech
   sections:
   - local: chapter6/introduction

chapters/en/chapter5/alternative_implementations.mdx

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+# Alternative ASR Implementations: Beyond Transformers
+
+While 🤗 Transformers provides an excellent foundation for ASR with models like Whisper, Moonshine, and Kyutai STT, the broader ASR ecosystem offers numerous optimized implementations that can significantly improve performance, reduce resource usage, and enable deployment in resource-constrained environments.
+
+This section explores high-performance alternatives, platform-specific optimizations, and specialized architectures that complement the transformers ecosystem while offering different trade-offs for speed, memory usage, and deployment scenarios.
+
+## High-Performance Optimized Implementations
+
+### whisper.cpp: C++ Port for Maximum Efficiency
+
+[whisper.cpp](https://github.com/ggml-org/whisper.cpp) is a C++ port of OpenAI's Whisper model that delivers exceptional performance improvements, particularly for CPU-based inference and edge deployment.
+
+#### Key Features:
+- **10x faster inference** on CPU compared to the original Python implementation
+- **Extremely low memory usage** - runs on devices with limited RAM
+- **Cross-platform support** - works on macOS, Linux, Windows, iOS, Android
+- **Apple Silicon optimization** - leverages Apple Neural Engine (ANE) for 3x additional speedup
+- **No dependencies** - self-contained C++ implementation
+
+#### Performance Characteristics:
+- **Memory**: Lowest VRAM consumption among all implementations
+- **Speed**: Excellent CPU performance, especially on Apple Silicon
+- **Accuracy**: ~75% transcription accuracy (some degradation from original)
+- **Deployment**: Ideal for edge devices and mobile applications
+
+#### Installation and Usage:
+
+```bash
+# Clone and build
+git clone https://github.com/ggml-org/whisper.cpp.git
+cd whisper.cpp
+make
+
+# Download a model (e.g., small model)
+bash ./models/download-ggml-model.sh small
+
+# Basic usage
+./main -m models/ggml-small.bin -f audio.wav
+```
+
+#### Python Bindings:
+
+```python
+import whisper_cpp
+
+# Initialize model
+model = whisper_cpp.Whisper("models/ggml-small.bin")
+
+# Transcribe audio
+result = model.transcribe("audio.wav")
+print(f"Transcription: {result['text']}")
+```
+
+#### When to Use whisper.cpp:
+- **Edge computing** and IoT devices
+- **Mobile applications** requiring offline processing
+- **CPU-only environments** without GPU acceleration
+- **Memory-constrained** systems
+- **Real-time processing** on low-power hardware
+
+### faster-whisper: GPU-Accelerated Performance
+
+[faster-whisper](https://github.com/SYSTRAN/faster-whisper) is a reimplementation of Whisper using CTranslate2, delivering significant performance improvements while maintaining full accuracy.
+
+#### Key Features:
+- **4x faster inference** than the original Whisper
+- **Same accuracy** as the original implementation
+- **Lower memory usage** through optimized memory management
+- **GPU and CPU support** with automatic optimization
+- **Streaming support** for real-time applications
+
+#### Performance Characteristics:
+- **Speed**: 4x faster than original, excellent GPU utilization
+- **Memory**: Reduced memory footprint
+- **Accuracy**: 100% accuracy preservation
+- **Deployment**: Ideal for server-based applications
+
+#### Installation and Usage:
+
+```bash
+pip install faster-whisper
+```
+
+```python
+from faster_whisper import WhisperModel
+
+# Initialize model with GPU support
+model = WhisperModel("small", device="cuda", compute_type="float16")
+
+# Transcribe audio
+segments, info = model.transcribe("audio.wav", beam_size=5)
+
+print(f"Detected language '{info.language}' with probability {info.language_probability}")
+
+for segment in segments:
+    print(f"[{segment.start:.2f}s -> {segment.end:.2f}s] {segment.text}")
+```
+
+#### Advanced Features:
+
+```python
+# Streaming transcription
+segments, info = model.transcribe(
+    "audio.wav",
+    beam_size=5,
+    language="en",
+    condition_on_previous_text=False,
+    temperature=0.0,
+    compression_ratio_threshold=2.4,
+    log_prob_threshold=-1.0,
+    no_speech_threshold=0.6,
+    word_timestamps=True,
+)
+
+# Voice Activity Detection (VAD)
+segments, info = model.transcribe(
+    "audio.wav", vad_filter=True, vad_parameters=dict(min_silence_duration_ms=500)
+)
+```
+
+
+## Platform-Specific Optimizations
+
+### MLX-Whisper: Apple Silicon Native Performance
+
+[MLX-Whisper](https://github.com/ml-explore/mlx-examples/tree/main/whisper) leverages Apple's MLX framework for optimal performance on Apple Silicon devices.
+
+#### Key Features:
+- **50% faster** than standard Whisper on Apple Silicon
+- **Native Metal performance** shading language integration
+- **Memory efficient** unified memory architecture utilization
+- **Energy efficient** for mobile and laptop deployment
+
+#### Performance Characteristics:
+- **Speed**: 2x faster on Apple Silicon devices
+- **Memory**: Optimized for unified memory architecture
+- **Accuracy**: Full accuracy preservation
+- **Deployment**: Exclusive to Apple Silicon (M1, M2, M3, M4)
+
+#### Installation and Usage:
+
+```bash
+pip install mlx-whisper
+```
+
+```python
+import mlx_whisper
+
+# Load model optimized for Apple Silicon
+model = mlx_whisper.load_model("small")
+
+# Transcribe with Metal acceleration
+result = model.transcribe("audio.wav")
+print(result["text"])
+```
+
+#### Lightning-Whisper-MLX: Maximum Apple Silicon Speed
+
+```python
+# Even faster MLX implementation
+from lightning_whisper_mlx import LightningWhisperMLX
+
+model = LightningWhisperMLX(model_name="small", batch_size=12, quant=None)
+result = model.transcribe("audio.wav")
+print(result["text"])
+```
+
+### WhisperKit: On-Device Apple Deployment
+
+[WhisperKit](https://github.com/argmaxinc/WhisperKit) provides production-ready on-device speech recognition for Apple platforms.
+
+#### Key Features:
+- **On-device processing** with privacy guarantees
+- **Core ML integration** for optimal performance
+- **iOS and macOS support** with native Swift APIs
+- **Real-time transcription** capabilities
+
+## Alternative Architectures
+
+### Conformer-Based Models: Edge Computing Focus
+
+Conformer architectures offer competitive performance with significantly lower computational requirements, making them ideal for edge deployment.
+
+#### Key Features:
+- **5.26x faster than real-time** on wearable devices
+- **Low power consumption** optimized for battery-powered devices
+- **Depthwise separable convolutions** reducing computational complexity from 32.8% to 4.0%
+- **Streaming capabilities** for real-time applications
+
+## Comprehensive Performance Comparison
+
+| Implementation | Speed vs Original | Memory Usage | Platform Focus | Accuracy vs Original | Use Case |
+|---------------|------------------|--------------|----------------|---------------------|----------|
+| **whisper.cpp** | 10x faster (CPU) | Very Low | Cross-platform | ~75% | Edge/Mobile |
+| **faster-whisper** | 4x faster | Low | GPU/CPU | 100% | Server/Cloud |
+| **MLX-Whisper** | 2x faster | Medium | Apple Silicon | 100% | Apple devices |
+| **Lightning-Whisper-MLX** | 10x faster | Medium | Apple Silicon | ~98% | Apple real-time |
+| **WhisperKit** | 3x faster | Low | Apple Mobile | 100% | iOS/macOS apps |
+| **Conformer** | 5.26x realtime | Very Low | Edge devices | Competitive | Wearables |
+
+## Deployment Strategies
+
+### Edge Computing Deployment
+
+#### Hardware Requirements:
+- **Minimum RAM**: 1GB for small models, 4GB for medium models
+- **CPU**: ARM Cortex-A78 or equivalent x86_64
+- **GPU**: Optional but recommended for real-time applications
+- **Storage**: 200MB for tiny models, 1GB for small models
+
+#### Optimization Techniques:
+1. **Model Quantization**: Reduce model size by 75% with minimal accuracy loss
+2. **Pruning**: Remove unnecessary parameters for faster inference
+3. **Knowledge Distillation**: Create smaller models that maintain accuracy
+4. **Memory Mapping**: Load models efficiently on resource-constrained devices
+
+### Mobile Deployment
+
+#### iOS/macOS with WhisperKit:
+
+```swift
+import WhisperKit
+
+let whisperKit = try await WhisperKit(model: "small")
+let transcription = try await whisperKit.transcribe(audioPath: "audio.wav")
+print(transcription.text)
+```
+
+#### Android with whisper.cpp:
+
+```java
+public class WhisperAndroid {
+    static {
+        System.loadLibrary("whisper_android");
+    }
+
+    public native String transcribe(String audioPath, String modelPath);
+}
+```
+
+### Real-Time Streaming
+
+#### Streaming with faster-whisper:
+
+```python
+from faster_whisper import WhisperModel
+import pyaudio
+import threading
+import queue
+
+
+class StreamingWhisper:
+    def __init__(self, model_name="small"):
+        self.model = WhisperModel(model_name, device="cuda", compute_type="float16")
+        self.audio_queue = queue.Queue()
+
+    def stream_transcribe(self):
+        while True:
+            if not self.audio_queue.empty():
+                audio_data = self.audio_queue.get()
+                segments, info = self.model.transcribe(
+                    audio_data,
+                    beam_size=5,
+                    language="en",
+                    condition_on_previous_text=False,
+                )
+
+                for segment in segments:
+                    print(f"[{segment.start:.2f}s -> {segment.end:.2f}s] {segment.text}")
+```
+
+## Integration with Existing Workflows
+
+### Combining with 🤗 Transformers:
+
+```python
+# Use faster-whisper for transcription, transformers for post-processing
+from faster_whisper import WhisperModel
+from transformers import pipeline
+
+# Fast transcription
+whisper_model = WhisperModel("small", device="cuda")
+segments, info = whisper_model.transcribe("audio.wav")
+
+# Post-processing with transformers
+classifier = pipeline("text-classification", model="distilbert-base-uncased")
+for segment in segments:
+    emotion = classifier(segment.text)
+    print(f"Text: {segment.text}, Emotion: {emotion}")
+```
+
+## Best Practices and Recommendations
+
+### Choosing the Right Implementation:
+
+1. **For Production Servers**: Use **faster-whisper** for the best balance of speed and accuracy
+2. **For Real-Time Applications**: Use **Lightning-Whisper-MLX**
+3. **For Edge/Mobile Devices**: Use **whisper.cpp** or **WhisperKit** (Apple)
+4. **For Apple Silicon**: Use **MLX-Whisper** or **Lightning-Whisper-MLX**
+5. **For Wearables**: Use **Conformer-based** models
+
+### Performance Optimization Tips:
+
+1. **Model Selection**: Choose the smallest model that meets your accuracy requirements
+2. **Quantization**: Use INT8 quantization for 4x speed improvement with minimal accuracy loss
+3. **Batching**: Process multiple audio files simultaneously when possible
+4. **Memory Management**: Use memory mapping for large models on resource-constrained devices
+5. **Preprocessing**: Ensure audio is properly formatted (16kHz, mono) before transcription
+
+## Summary
+
+The ASR ecosystem extends far beyond transformers-based implementations, offering specialized solutions for different deployment scenarios:
+
+- **whisper.cpp** excels in edge computing and mobile deployment with minimal resource usage
+- **faster-whisper** provides the best balance of speed and accuracy for server deployments
+- **MLX-Whisper** optimizes performance specifically for Apple Silicon devices
+- **Conformer-based models** offer efficient alternatives for resource-constrained environments
+
+The choice between these implementations depends on your specific requirements for speed, accuracy, memory usage, and deployment environment. Many applications benefit from using multiple implementations in combination, leveraging the strengths of each for different components of the speech recognition pipeline.
+
+In the next section, we'll explore how to evaluate these different implementations and choose the right metrics for your specific use case.