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easier to maintain this data set and upload additional notes as I go along.", - "corrected_transcript": "", - "parameters": { - "speaker_info": "", - "audio_quality": "", - "environment": "", - "corrections_needed": [] - }, - "status": "pending" - } -] \ No newline at end of file diff --git a/audio/.claude/settings.local.json b/audio/.claude/settings.local.json deleted file mode 100644 index 681e385e457b498fbfe00e10e71d311d1f273680..0000000000000000000000000000000000000000 --- a/audio/.claude/settings.local.json +++ /dev/null @@ -1,11 +0,0 @@ -{ - "permissions": { - "allow": [ - "Bash(git checkout:*)", - "Bash(git branch:*)", - "Bash(git push:*)" - ], - "deny": [], - "ask": [] - } -} \ No newline at end of file diff --git a/audio/1.mp3 b/audio/1.mp3 deleted file mode 100644 index 1c3e9e1d367a77a1ee7dc639580143480a565c9a..0000000000000000000000000000000000000000 --- a/audio/1.mp3 +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid 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+0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:b6620fddb93d84abdf816243eb4f71f4cbd661a4f4e5eccd305301fb1c60660b -size 1619756 diff --git a/audio/99.mp3 b/audio/99.mp3 deleted file mode 100644 index 255d2cd69868f45ce7fa13260eb727ff9be1e25e..0000000000000000000000000000000000000000 --- a/audio/99.mp3 +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:3dcfbbe66126a02579fd51663cbf86397378950569c163743a1429a3b38f928d -size 907244 diff --git a/calculated-parameters.md b/calculated-parameters.md deleted file mode 100644 index 68a9848ffba1832bc3fb929e8bfc169b2f714800..0000000000000000000000000000000000000000 --- a/calculated-parameters.md +++ /dev/null @@ -1,136 +0,0 @@ -# Calculated Parameters - -This document outlines parameters that will be calculated programmatically from audio files and metadata. These automated calculations will enhance the dataset's research value without requiring manual annotation. - -## Temporal Parameters - -### Recording Timestamp -- **Description**: Exact timestamp when the voice note was recorded -- **Format**: ISO 8601 format (YYYY-MM-DDTHH:MM:SSZ) -- **Source**: Audio file metadata or filename parsing -- **Research Value**: Enables temporal analysis of voice note patterns and quality variations over time -- **Implementation**: Extract from file creation date, EXIF data, or filename timestamps - -### Time of Day -- **Description**: Categorical time period based on recording timestamp -- **Options**: - - **Morning**: 06:00 - 11:59 - - **Afternoon**: 12:00 - 17:59 - - **Evening**: 18:00 - 21:59 - - **Night**: 22:00 - 05:59 -- **Source**: Derived from recording timestamp -- **Research Value**: Correlate with voice quality, background noise patterns, and speech characteristics that may vary throughout the day -- **Implementation**: Programmatic categorization based on hour component of timestamp - -## Audio Signal Analysis - -### Signal-to-Noise Ratio (SNR) -- **Description**: Computed dB ratio between speech signal and background noise -- **Measurement**: Automatic calculation from audio analysis using voice activity detection -- **Research Value**: Critical for understanding STT performance degradation in noisy environments -- **Implementation**: FFT analysis with VAD segmentation - -### Audio Duration -- **Description**: Precise length of the audio file -- **Format**: Seconds (floating point) -- **Source**: Audio file header analysis -- **Research Value**: Correlate recording length with transcription accuracy and user behavior patterns - -### Average dB Level -- **Description**: Root Mean Square (RMS) amplitude level of the audio -- **Measurement**: dB relative to full scale (dBFS) -- **Research Value**: Understand recording volume consistency and microphone gain settings - -### Peak dB Level -- **Description**: Maximum amplitude peak in the audio -- **Measurement**: Peak dB level relative to full scale -- **Research Value**: Identify clipping and dynamic range issues - -### Dynamic Range -- **Description**: Difference between loudest and quietest audio segments -- **Measurement**: dB difference between peak and RMS levels -- **Research Value**: Indicates recording quality and compression effects - -## Linguistic Analysis - -### Word Count -- **Description**: Total number of words in the transcript -- **Source**: Tokenized transcript analysis -- **Research Value**: Correlate transcript length with accuracy and recording duration - -### Character Count -- **Description**: Total number of characters in the transcript (excluding spaces) -- **Source**: String length analysis of transcript -- **Research Value**: Alternative length metric for analysis - -### Estimated Speaking Rate -- **Description**: Words per minute calculation -- **Calculation**: (Word Count / Audio Duration in minutes) -- **Research Value**: Identify fast/slow speech patterns that may affect STT accuracy - -### Speaking Rate Classification -- **Description**: Categorical classification of speaking speed -- **Options**: - - **Very Slow**: < 100 WPM - - **Slow**: 100-130 WPM - - **Normal**: 130-160 WPM - - **Fast**: 160-190 WPM - - **Very Fast**: > 190 WPM -- **Source**: Derived from estimated speaking rate -- **Research Value**: Group recordings by speech tempo for targeted analysis - -## File Metadata - -### File Size -- **Description**: Size of the audio file in bytes -- **Source**: File system metadata -- **Research Value**: Understand compression ratios and storage requirements - -### Audio Format -- **Description**: Audio codec and container format -- **Examples**: MP3, WAV, M4A, OGG -- **Source**: File header analysis -- **Research Value**: Account for compression artifacts in STT performance - -### Sample Rate -- **Description**: Audio sampling frequency -- **Units**: Hz (e.g., 44100, 48000, 16000) -- **Source**: Audio file header -- **Research Value**: Correlate sampling rate with transcription quality - -### Bit Depth -- **Description**: Audio bit depth/resolution -- **Units**: bits (e.g., 16, 24, 32) -- **Source**: Audio file header -- **Research Value**: Understand dynamic range capabilities - -## Quality Metrics - -### Word Error Rate (WER) -- **Description**: Calculated WER comparing AI transcript to corrected transcript -- **Calculation**: (Substitutions + Deletions + Insertions) / Total Reference Words -- **Source**: Comparison between original and corrected transcripts -- **Research Value**: Primary STT performance metric - -### Character Error Rate (CER) -- **Description**: Character-level error rate -- **Calculation**: Similar to WER but at character level -- **Source**: Character-level comparison of transcripts -- **Research Value**: More granular accuracy assessment - -## Implementation Notes - -- All calculations will be performed automatically during dataset preprocessing -- Timestamps will be normalized to UTC for consistency -- Error rate calculations require both original and corrected transcripts -- Audio analysis parameters require signal processing libraries (librosa, pyAudio, etc.) -- Results will be stored in dataset metadata JSON files -- Failed calculations will be marked as null/missing values with error logging - -## Future Enhancements - -- **Spectral Analysis**: Frequency domain characteristics -- **Voice Activity Detection**: Percentage of audio containing speech -- **Pause Analysis**: Distribution of silent segments -- **Prosodic Features**: Pitch, tempo, and rhythm analysis -- **Language Detection**: Automatic language identification confidence scores \ No newline at end of file diff --git a/candidate-parameters.md b/candidate-parameters.md deleted file mode 100644 index 057793d7af4d78da928912c82dd479ae67b10b38..0000000000000000000000000000000000000000 --- a/candidate-parameters.md +++ /dev/null @@ -1,138 +0,0 @@ -# Candidate Parameters for Future Implementation - -This document outlines additional parameters that could enhance the dataset's research value for STT evaluation and fine-tuning. These parameters are candidates for future annotation phases. - -## Audio Signal Quality - -### Signal-to-Noise Ratio (SNR) -- **Description**: Computed dB ratio between speech signal and background noise -- **Measurement**: Automatic calculation from audio analysis -- **Research Value**: Critical for understanding STT performance degradation - -### Audio Clipping Detection -- **Description**: Whether audio peaks are clipped or distorted -- **Options**: [None, Minimal (<1% samples), Moderate (1-5%), Severe (>5%)] -- **Research Value**: Identifies recordings with digital distortion artifacts - -### Dynamic Range -- **Description**: Difference between loudest and quietest audio segments -- **Measurement**: dB difference between peak and RMS levels -- **Research Value**: Indicates recording quality and compression effects - -### Frequency Response Issues -- **Description**: Low-pass filtering effects from phone microphones -- **Options**: [Full Range, Slight Roll-off, Moderate Filtering, Heavy Filtering] -- **Research Value**: Understanding microphone limitations on STT accuracy - -## Speech Characteristics - -### Accent/Dialect Strength -- **Description**: How pronounced regional speech patterns are -- **Options**: [None/Standard, Slight, Moderate, Strong, Very Strong] -- **Research Value**: Evaluating STT robustness across dialects - -### Emotional State -- **Description**: Speaker's emotional state affecting speech patterns -- **Options**: [Calm, Excited, Frustrated, Tired, Stressed, Other] -- **Research Value**: Understanding how emotion affects STT accuracy - -### Speech Hesitations -- **Description**: Frequency of disfluencies and self-corrections -- **Options**: [None, Rare (<5%), Occasional (5-15%), Frequent (15-30%), Very Frequent (>30%)] -- **Research Value**: Testing STT handling of natural speech patterns - -### Articulation Quality -- **Description**: Clarity of speech production -- **Options**: [Very Clear, Clear, Slightly Unclear, Unclear, Mumbled] -- **Research Value**: Correlating articulation with transcription accuracy - -## Linguistic Complexity - -### Proper Noun Density -- **Description**: Percentage of words that are names, places, or brands -- **Calculation**: (Proper nouns / Total words) × 100 -- **Research Value**: Evaluating STT performance on out-of-vocabulary terms - -### Domain-Specific Vocabulary -- **Description**: Presence of technical, specialized, or foreign terminology -- **Options**: [None, Low (<5%), Moderate (5-15%), High (15-30%), Very High (>30%)] -- **Research Value**: Testing STT adaptation to specialized domains - -### Sentence Structure Complexity -- **Description**: Grammatical complexity of spoken sentences -- **Options**: [Simple, Compound, Complex, Very Complex, Fragmented] -- **Research Value**: Understanding parsing challenges for STT systems - -### Out-of-Vocabulary (OOV) Rate -- **Description**: Estimated percentage of words not in common STT vocabularies -- **Calculation**: Based on comparison with standard word lists -- **Research Value**: Predicting STT performance on novel content - -## Recording Context - -### Device Movement -- **Description**: Movement pattern during recording -- **Options**: [Stationary, Slight Movement, Walking, In Vehicle, Other Motion] -- **Research Value**: Understanding motion effects on audio quality - -### Distance from Microphone -- **Description**: Estimated speaker distance from recording device -- **Options**: [Close (<6"), Normal (6-18"), Far (18-36"), Very Far (>36")] -- **Research Value**: Evaluating near-field vs. far-field performance - -### Recording App/Service -- **Description**: Application used for recording (affects preprocessing) -- **Options**: [Voicenotes.com, Native Voice Memo, WhatsApp, Zoom, Other] -- **Research Value**: Understanding preprocessing effects on STT - -### Time of Day -- **Description**: When the recording was made -- **Options**: [Early Morning, Morning, Afternoon, Evening, Late Night] -- **Research Value**: Correlating with voice fatigue and background patterns - -## STT Challenge Categories - -### Homophones Present -- **Description**: Words that sound alike but have different meanings -- **Detection**: Manual annotation or automated detection -- **Research Value**: Testing semantic disambiguation in STT - -### Code-Switching -- **Description**: Mixing languages within the same utterance -- **Options**: [None, Occasional Words, Phrases, Frequent Switching] -- **Research Value**: Multilingual STT robustness evaluation - -### Incomplete Sentences -- **Description**: Frequency of trailing off or interrupted thoughts -- **Options**: [None, Rare, Occasional, Frequent, Mostly Incomplete] -- **Research Value**: Natural speech pattern handling - -### Number/Date Format Complexity -- **Description**: Complexity of numeric and temporal expressions -- **Examples**: "March 3rd" vs "3/3/24", "twenty-five" vs "25" -- **Options**: [Simple, Mixed Formats, Complex, Ambiguous] -- **Research Value**: Numeric transcription accuracy evaluation - -## Implementation Priority - -### High Priority -- Signal-to-Noise Ratio (auto-computed) -- Emotional State (manual annotation) -- Proper Noun Density (semi-automated) - -### Medium Priority -- Device Movement -- Speech Hesitations -- Domain-Specific Vocabulary - -### Low Priority (Research Interest) -- Frequency Response Issues -- Code-Switching -- Sentence Structure Complexity - -## Notes - -- Parameters marked as "auto-computed" can be calculated programmatically -- Manual annotation parameters should be added progressively -- Consider inter-annotator agreement studies for subjective parameters -- Some parameters may correlate strongly and could be combined or prioritized \ No newline at end of file diff --git a/dataset_metadata.json b/dataset_metadata.json deleted file mode 100644 index bdff2164c81fce31f9d79e47dc3e60e8126137cd..0000000000000000000000000000000000000000 --- a/dataset_metadata.json +++ /dev/null @@ -1,7 +0,0 @@ -[ - { - "id": "1", - "audio": "audio/1.mp3", - "ai_transcript": "aitranscripts/1.txt" - } -] \ No newline at end of file diff --git a/label_studio_config.xml b/label_studio_config.xml deleted file mode 100644 index 73da4dca01bf6de76ec9d3ee9dd5893a3d25dcf0..0000000000000000000000000000000000000000 --- a/label_studio_config.xml +++ /dev/null @@ -1,181 +0,0 @@ - -
-