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update multi-hans whisper-qwen-1.5b results (#1657)
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### whisper_llm_zh finetuning results
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### whisper_llm_zh finetuning results
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| Training Dataset | Speech Encoder | LLM | Projector |Comment | CER |
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|Model| Training Dataset | Speech Encoder | LLM | Projector |
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| -------------------------| ----------------|------|--------------------------------------------------|-----|--|
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|-| -------------------------| ----------------|------|---------------|
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| Aishell1 | whisper-large-v2-aishell1-ft, freeze| Qwen2-1.5B-Instruct, LoRA | Linear, 8x downsample| [yuekai/icefall_asr_aishell_whisper_qwen2_1.5B](https://huggingface.co/yuekai/icefall_asr_aishell_whisper_qwen2_1.5B) | Aishell1 Test 3.62% |
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|[yuekai/icefall_asr_aishell_whisper_qwen2_1.5B](https://huggingface.co/yuekai/icefall_asr_aishell_whisper_qwen2_1.5B) | Aishell1 | whisper-large-v2-aishell1-ft, freeze| Qwen2-1.5B-Instruct, LoRA | Linear, 8x downsample|
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<!-- | Multi-hans-zh | whisper-large-v2-multi-hans-ft, freeze| Qwen2-1.5B-Instruct, LoRA | Linear, 8x downsample| WIP ||
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| [yuekai/icefall_asr_multi-hans_whisper_qwen2_1.5B](https://huggingface.co/yuekai/icefall_asr_multi-hans_whisper_qwen2_1.5B) |Multi-hans-zh | whisper-large-v2-multi-hans-ft, freeze| Qwen2-1.5B-Instruct, LoRA | Linear, 8x downsample||
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| Multi-hans-zh | whisper-large-v2-multi-hans-ft, freeze| Qwen2-7B-Instruct, LoRA | Linear, 8x downsample| WIP || -->
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CER Details:
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| Model | [yuekai/icefall_asr_aishell_whisper_qwen2_1.5B](https://huggingface.co/yuekai/icefall_asr_aishell_whisper_qwen2_1.5B) | [yuekai/icefall_asr_multi-hans_whisper_qwen2_1.5B](https://huggingface.co/yuekai/icefall_asr_multi-hans_whisper_qwen2_1.5B) |
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|-------|------------------------------------------------|----------------------------------------------------|
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| Split | Greedy Search | Greedy Search |
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| aishell-1 dev | - | 0.66 |
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| aishell-1 test | 3.62 | 0.68 |
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| aishell-2 dev | - | 2.67 |
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| aishell-2 test | - | 2.94 |
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| aishell-4 test | - | 16.20 |
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| alimeeting eval | - | 30.86 |
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| alimeeting test | - | 40.50 |
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| magicdata dev | - | 2.50 |
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| magicdata test | - | 1.70 |
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| kespeech-asr dev phase1 | - | 6.22 |
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| kespeech-asr dev phase2 | - | 2.18 |
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| kespeech-asr test | - | 6.59 |
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| WenetSpeech dev | - | 4.59 |
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| WenetSpeech test_meeting | - | 6.41 |
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| WenetSpeech tes_net | - | 6.63 |
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| SPEECHIO Avg 001-026 | - | 4.80 |
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Command for training is:
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Command for training is:
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```bash
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```bash
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pip install -r whisper_llm_zh/requirements.txt
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pip install -r whisper_llm_zh/requirements.txt
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@ -16,12 +16,11 @@
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# See the License for the specific language governing permissions and
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# limitations under the License.
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"""
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"""
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This file uses whisper and zipformer decoding results to generate fusion decoding results.
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This file uses speech io offcial pipline to normalize the decoding results.
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Since whisper model is more likely to make deletion errors and zipformer model is more likely to make substitution and insertion errors,
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https://github.com/SpeechColab/Leaderboard/blob/master/utils/textnorm_zh.py
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we trust whisper model when it makes substitution and insertion errors and trust zipformer model when it makes deletion errors.
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Usage:
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Usage:
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python whisper_zipformer_fusion.py --model-log-dir ./whisper_decoding_log_dir --output-log-dir ./results_norm
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python normalize_results.py --model-log-dir ./whisper_decoding_log_dir --output-log-dir ./results_norm
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"""
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"""
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import argparse
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import argparse
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