From 06f10a2f8677303f21f2b992b74f72401116d6a5 Mon Sep 17 00:00:00 2001 From: Vijay Janapa Reddi Date: Mon, 13 Jan 2025 08:22:14 -0500 Subject: [PATCH] Addresses Bravo's feedback --- .../data_engineering/data_engineering.qmd | 17 +++++++++++------ .../images/png/kws_spectrogram.png | Bin 0 -> 1013438 bytes contents/core/introduction/introduction.qmd | 2 +- 3 files changed, 12 insertions(+), 7 deletions(-) create mode 100644 contents/core/data_engineering/images/png/kws_spectrogram.png diff --git a/contents/core/data_engineering/data_engineering.qmd b/contents/core/data_engineering/data_engineering.qmd index eec6e4cb..f0762f23 100644 --- a/contents/core/data_engineering/data_engineering.qmd +++ b/contents/core/data_engineering/data_engineering.qmd @@ -342,11 +342,11 @@ Once ingestion is complete and data is validated, it is typically loaded into a ### Case Study: KWS -A production KWS system typically employs both streaming and batch ingestion patterns. The streaming pattern handles real-time audio data from active devices, where wake words must be detected with minimal latency. This requires careful implementation of pub/sub mechanisms—for example, using Kafka-like streams to buffer incoming audio data and enable parallel processing across multiple inference servers. +A production KWS system typically employs both streaming and batch ingestion patterns. The streaming pattern handles real-time audio data from active devices, where wake words must be detected with minimal latency. This requires careful implementation of pub/sub mechanisms—for example, using Apache Kafka-like streams to buffer incoming audio data and enable parallel processing across multiple inference servers. Simultaneously, the system processes batch data for model training and updates. This includes ingesting new wake word recordings from crowdsourcing efforts, synthetic data from voice generation systems, and validated user interactions. The batch processing typically follows an ETL pattern, where audio data is preprocessed (normalized, filtered, segmented) before being stored in a format optimized for model training. -KWS systems must integrate data from diverse sources, such as real-time audio streams from deployed devices, rrowdsourced recordings from data collection platforms etc Each source presents unique challenges. Real-time audio streams require rate limiting to prevent system overload during usage spikes. Crowdsourced data needs robust validation to ensure recording quality and correct labeling. Synthetic data must be verified for realistic representation of wake word variations. +KWS systems must integrate data from diverse sources, such as real-time audio streams from deployed devices, crowdsourced recordings from data collection platforms etc. Each source presents unique challenges. Real-time audio streams require rate limiting to prevent system overload during usage spikes. Crowdsourced data needs robust validation to ensure recording quality and correct labeling. Synthetic data must be verified for realistic representation of wake word variations. KWS systems employ sophisticated error handling mechanisms due to the nature of voice interaction. When processing real-time audio, dead letter queues store failed recognition attempts for analysis, helping identify patterns in false negatives or system failures. Data validation becomes particularly important for maintaining wake word detection accuracy—incoming audio must be checked for quality issues like clipping, noise levels, and appropriate sampling rates. @@ -429,17 +429,22 @@ Effective data processing is fundamental to the success of ML systems. By carefu A KWS system requires careful cleaning of audio recordings to ensure reliable wake word detection. Raw audio data often contains various imperfections—background noise, clipped signals, varying volumes, and inconsistent sampling rates. For example, when processing the wake word "Alexa," the system must clean recordings to standardize volume levels, remove ambient noise, and ensure consistent audio quality across different recording environments, all while preserving the essential characteristics that make the wake word recognizable. -Building on clean data, quality assessment becomes crucial for KWS systems. Quality metrics for KWS data are uniquely focused on audio characteristics, including signal-to-noise ratio (SNR), audio clarity scores, and speaking rate consistency. For instance, a KWS quality assessment pipeline might automatically flag recordings where background noise exceeds acceptable thresholds or where the wake word is spoken too quickly or unclearly, ensuring only high-quality samples are used for model training. +Building on clean data, quality assessment becomes important for KWS systems. Quality metrics for KWS data are uniquely focused on audio characteristics, including signal-to-noise ratio (SNR), audio clarity scores, and speaking rate consistency. For instance, a KWS quality assessment pipeline might automatically flag recordings where background noise exceeds acceptable thresholds or where the wake word is spoken too quickly or unclearly, ensuring only high-quality samples are used for model training. + +These quality metrics must be carefully calibrated to reflect real-world operating conditions. A robust training dataset incorporates both pristine recordings and samples containing controlled levels of environmental variations. For instance, while recordings with signal-masking interference are excluded, the dataset should include samples with measured background acoustics, variable speaker distances, and concurrent speech or other forms of audio signals. This approach to data diversity ensures the model maintains wake word detection reliability across the full spectrum of deployment environments and acoustic conditions. Once quality is assured, transforming audio data for KWS involves converting raw waveforms into formats suitable for ML models. The typical transformation pipeline converts audio signals into spectrograms[^defn-spectrogram] or mel-frequency cepstral coefficients (MFCCs)[^defn-mfcc], standardizing the representation across different recording conditions. This transformation must be consistently applied across both training and inference, often with additional considerations for real-time processing on edge devices. [^defn-spectrogram]: **Spectrogram:** A visual representation of the spectrum of frequencies in a signal as it varies over time, commonly used in audio processing. - [^defn-mfcc]: **Mel-Frequency Cepstral Coefficients (MFCCs):** Features extracted from audio signals that represent the short-term power spectrum, widely used in speech and audio analysis. +@fig-spectrogram-example illustrates this transformation process. The top panel is a raw waveform of a simulated audio signal, which consists of a sine wave mixed with noise. This time-domain representation highlights the challenges posed by real-world recordings, where noise and variability must be addressed. The middle panel shows the spectrogram of the signal, which maps its frequency content over time. The spectrogram provides a detailed view of how energy is distributed across frequencies, making it easier to analyze patterns that could influence wake word recognition, such as the presence of background noise or signal distortions The bottom panel shows the MFCCs, derived from the spectrogram. These coefficients compress the audio information into a format that emphasizes speech-related characteristics, making them well-suited for KWS tasks. + +![KWS data processing of an audio signal (top panel) represented in a spectrogram (middle panel) showing the energy distribution across time and frequency, along with the corresponding MFCCs (bottom panel) that capture perceptually relevant features.](images/png/kws_spectrogram.png){#fig-spectrogram-example} + With transformed data in hand, feature engineering for KWS focuses on extracting characteristics that help distinguish wake words from background speech. Engineers might create features capturing tonal variations, speech energy patterns, or temporal characteristics. For the wake word "Alexa," features might include energy distribution across frequency bands, pitch contours, and duration patterns that characterize typical pronunciations. -Finally, bringing all these elements together, KWS processing pipelines must handle both batch processing for training and real-time processing for inference. The pipeline typically includes stages for audio preprocessing, feature extraction, and quality filtering. Importantly, these pipelines must be designed to operate efficiently on edge devices while maintaining consistent processing steps between training and deployment. +In practice, bringing all these elements together, KWS processing pipelines must handle both batch processing for training and real-time processing for inference. The pipeline typically includes stages for audio preprocessing, feature extraction, and quality filtering. Importantly, these pipelines must be designed to operate efficiently on edge devices while maintaining consistent processing steps between training and deployment. ## Data Labeling @@ -707,7 +712,7 @@ The performance characteristics of feature stores are particularly intriguing fr Feature stores also introduce interesting challenges in terms of data freshness and update strategies. Some features may need to be updated in real-time (e.g., current user session information), while others might be updated on a daily or weekly basis (e.g., aggregated customer behavior metrics). Managing these different update frequencies and ensuring that the most up-to-date features are always available for inference can be complex. -From a storage perspective, feature stores often leverage a combination of different storage technologies to meet their diverse requirements. This might include columnar storage formats like Parquet for the offline store, in-memory databases or key-value stores for the online store, and streaming platforms like Kafka for real-time feature updates. +From a storage perspective, feature stores often leverage a combination of different storage technologies to meet their diverse requirements. This might include columnar storage formats like Parquet for the offline store, in-memory databases or key-value stores for the online store, and streaming platforms like Apache Kafka for real-time feature updates. ### Caching Strategies diff --git a/contents/core/data_engineering/images/png/kws_spectrogram.png b/contents/core/data_engineering/images/png/kws_spectrogram.png new file mode 100644 index 0000000000000000000000000000000000000000..1f976b13ab9d006bc9c590a9529a92e10612f6eb GIT binary patch literal 1013438 zcmdSAcT`hb)IEyWPy|#IFG^7q6cA8)6)g0oROw0&E!5D9ilQRDcNIb}0wI(DQ4o>d zTSAD4)R0g@C&1g$>;1m>#{2V)H^whxFgPJ6`|Pv!T64`g_ugfcrybQB(_kciM zL% zebL&4zv|`F_d-Hxt~6|B`X2avzb2BRk(-_(k`f?4($bPHk|!&YlA@crTf6j6So7p* zFoFMhJ*d@!N&L@S>QU3PC%pf+zp&GO0kQaBFX(UHbx?*N*!AbHFR?;S5sH#R+aPL z&_f69d44{0+zr!$znLed!nE%?)vVz8{Dc# zj~+E{45*#(Uy{SQcL?l&&+m=@Nt2M}tN$+Tdv;8{ZzXJTbaJH@GLZYSR_yh;?d|P4 zLMf^~=$PNeV!{62Zjr}qw|TX`g$2}${AVUk%B*+(oym8TTb)fW>Gcf^a_ZUH#$A$3lCNE+Z?1XJ 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architectures and scales of ML systems demonstrate their potential t [FarmBeats](https://www.microsoft.com/en-us/research/project/farmbeats-iot-agriculture/), a project developed by Microsoft Research, shown in @fig-farmbeats-overview is a significant advancement in the application of machine learning to agriculture. This system aims to increase farm productivity and reduce costs by leveraging AI and IoT technologies. FarmBeats exemplifies how edge and embedded ML systems can be deployed in challenging, real-world environments to solve practical problems. By bringing ML capabilities directly to the farm, FarmBeats demonstrates the potential of distributed AI systems in transforming traditional industries. -![Microsoft Farmbeats: AI, Edge & IoT for Agriculture.](./images/png/farmbeats.png){#fig-farmbeats-overview} +![Microsoft FarmBeats: AI, Edge & IoT for Agriculture.](./images/png/farmbeats.png){#fig-farmbeats-overview} #### Data Aspects