<jupyter_start><jupyter_text>At first, I thought QuartzNet-15x5 would performe quite well on our data. But I overkilled the model with too agressive augmentation probrobabilities (it was 0.3 that some augmentation in sequence will be applied to data). After chagnging probabilities, I saw that the QuartzNet would require a [lot of time](https://wandb.ai/khaykingleb/asr_project/reports/Untitled-Report--VmlldzoxMTU0Mzg5) to train for near-SOTA results. Thus, due to time scarcity I decided to implement Deepspeech.  Deepspeech We will train the model in several steps. 1) Train the Deepspeech on LJ-speech dataset for around 45 epochs.<jupyter_code>%run ASR/train.py -c ASR/configs/deepspeech_ljspeech_config.json<jupyter_output><empty_output><jupyter_text>Achieved the 19 WER on this dataset. See the [report](https://wandb.ai/khaykingleb/asr_project/reports/Deepspeech-on-LJ-Dataset--VmlldzoxMTYyODk0?accessToken=<KEY>). 2) Fine-tune the Deepspeech on Librispeech clean dataset for around 35 epochs.<jupyter_code>!wget https://www.dropbox.com/s/3nznm2lwt58vzl0/deepspeech_ljspeech.pth?dl=0 \
    -P /trained_models \
    -O deepspeech_ljspeech.pth
%run ASR/train.py -c ASR/configs/deepspeech_librispeech_config.json<jupyter_output><empty_output><jupyter_text>It [can't train](https://wandb.ai/khaykingleb/asr_project/reports/Deepspeech-on-Librispeech-Dataset--VmlldzoxMTYzMDI4?accessToken=ds29hze7tlu15yowv7fywnxb06wqv6s7u8lz94g5xl5yqelp9a6wt5pxln710mmx), getting 18 CER, so we move on to the next dataset. 3) Fine-tune the Deepspeech on Common Voice dataset.<jupyter_code>!wget https://www.dropbox.com/s/3nznm2lwt58vzl0/deepspeech_librispeech.pth?dl=0 \
    -P /trained_models \
    -O deepspeech_librispeech.pth
%run ASR/train.py -c ASR/configs/deepspeech_common_voice_config.json<jupyter_output><empty_output><jupyter_text>I am currently at this stage. Dunno if i will make it to the end, but we will see.See the current [report](https://wandb.ai/khaykingleb/asr_project/reports/Deepspeech-on-Common-Voice-Dataset---VmlldzoxMTYyOTk3?accessToken=<KEY>). 4) Fine-tune the Deepspeech on other Librispeech.<jupyter_code><jupyter_output><empty_output><jupyter_text>Results Currently, I use model that is trained on first and second steps:<jupyter_code>!wget https://www.dropbox.com/s/154ajtbmo5bam19/checkpoint.pth?dl=0 \
    -P ASR/default_test_model/ \
    -O checkpoint.pth
%run ASR/test.py \
   -c ASR/default_test_model/config.json \
   -r ASR/default_test_model/checkpoint.pth \
   -o ASR/default_test_model/result.json
import json
file = open('ASR/default_test_model/result.json')
result = json.load(file)
argmax_cer = 0
argmax_wer = 0
beam_search_cer = 0
beam_search_wer = 0 

for i in range(len(result)):
    argmax_cer += result[i]['argmax_cer']
    argmax_wer += result[i]['argmax_wer']
    beam_search_cer += result[i]['beam_search_cer']
    beam_search_wer += result[i]['beam_search_wer']
print(f"Argmax CER: {argmax_cer / len(result):.4}")
print(f"Argmax WER: {argmax_wer / len(result):.4}")
print(f"Beam Search CER: {beam_search_cer / len(result):.4}")
print(f"Beam Search WER: {beam_search_wer / len(result):.4}")<jupyter_output>Beam Search CER: 20.44
Beam Search WER: 56.92
{'fraction_non_alphanumeric': 0.1109370103415857, 'fraction_numerical': 0.024443748041366342, 'mean_word_length': 5.540983606557377, 'pattern_counts': {'":': 0, '<': 22, '<?xml version=': 0, '>': 22, 'https://': 7, 'lorem ipsum': 0, 'www.': 3, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '5', 'dir': 'jupyter-structured-clean-dedup', 'id': '547155', 'license': 'MIT', 'n_tokens_mistral': 1302, 'n_tokens_neox': 1250, 'n_words': 251, 'path': 'notebooks/notebook.ipynb', 'repo_name': 'khaykingleb/Automatic-Speech-Recognition', 'size': '3309'}