83 lines
1.6 KiB
Markdown
83 lines
1.6 KiB
Markdown
# Benchmark Dataset Preparation Guide
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This guide describes how to download and prepare the SeedTTS test dataset for benchmarking Qwen-Omni models.
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## Prerequisites
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- Python 3.8+
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- `gdown` for downloading from Google Drive
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- Access to the benchmark scripts
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## Steps
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### 1. Navigate to the Dataset Directory
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```bash
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cd benchmarks/build_dataset
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```
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### 2. Install Dependencies
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```bash
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pip install gdown
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```
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### 3. Download the SeedTTS Test Dataset
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Download the dataset from Google Drive:
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```bash
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gdown 1GlSjVfSHkW3-leKKBlfrjuuTGqQ_xaLP
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```
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### 4. Extract the Dataset
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```bash
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tar -xf seedtts_testset.tar
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```
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### 5. Prepare the Metadata File
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Copy the English metadata file to the working directory:
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```bash
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cp seedtts_testset/en/meta.lst meta.lst
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```
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### 6. Extract Prompts
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Extract the first N prompts from the metadata file:
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```bash
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# Extract top 100 prompts (adjust -n for different amounts)
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python extract_tts_prompts.py -i meta.lst -o top100.txt -n 100
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```
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**Options:**
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- `-i, --input`: Input metadata file (default: `meta.lst`)
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- `-o, --output`: Output prompts file (default: `prompts.txt`)
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- `-n, --num-lines`: Number of prompts to extract (required)
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### 7. Clean Up (Optional)
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Remove temporary files to save disk space:
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```bash
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rm -rf seedtts_testset
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rm seedtts_testset.tar
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rm meta.lst
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```
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## Quick Start (All-in-One)
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```bash
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# Full setup and benchmark
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cd benchmarks/build_dataset
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pip install gdown
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gdown 1GlSjVfSHkW3-leKKBlfrjuuTGqQ_xaLP
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tar -xf seedtts_testset.tar
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cp seedtts_testset/en/meta.lst meta.lst
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python extract_tts_prompts.py -i meta.lst -o top100.txt -n 100
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rm -rf seedtts_testset seedtts_testset.tar meta.lst
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```
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