main()
  File "generate_lm.py", line 201, in main
    build_lm(args, data_lower, vocab_str)
  File "generate_lm.py", line 126, in build_lm
    binary_path,
  File "/usr/lib/python3.7/subprocess.py", line 363, in check_call
    raise CalledProcessError(retcode, cmd)
subprocess.CalledProcessError: Command '['/content/DeepSpeech/native_client/kenlm/build/bin/build_binary', '-a', '255', '-q', '8', '-v', 'trie', '/content/DeepSpeech/data/lm/lm_filtered.arpa', '/content/DeepSpeech/data/lm/lm.binary']' died with .```

Calling the script generate_lm.py like this :

```! python3 generate_lm.py --input_txt hindi_tokens.txt --output_dir /content/DeepSpeech/data/lm --top_k 500000 --kenlm_bins /content/DeepSpeech/native_client/kenlm/build/bin/ --arpa_order 5 --max_arpa_memory "85%" --arpa_prune "0|0|1" --binary_a_bits 255 --binary_q_bits 8 --binary_type trie```

%cd /content/DeepSpeech
!python3 DeepSpeech.py \
--drop_source_layers 2 --scorer /content/DeepSpeech/data/lm/kenlm-nigerian.scorer\
 --train_cudnn True --early_stop True --es_epochs 6 --n_hidden 2048 --epochs 5 \
  --export_dir /content/models/ --checkpoint_dir /content/model_checkpoints/ \
  --train_files /content/train.csv --dev_files /content/dev.csv --test_files /content/test.csv \
  --learning_rate 0.0001 --train_batch_size 64 --test_batch_size 32 --dev_batch_size 32 --export_file_name 'he_model_5' \
  --max_to_keep 3

(0) Invalid argument: Not enough time for target transition sequence (required: 28, available: 24)0You can turn this error into a warning by using the flag ignore_longer_outputs_than_inputs
(1) Invalid argument: Not enough time for target transition sequence (required: 28, available: 24)0You can turn this error into a warning by using the flag ignore_longer_outputs_than_inputs

subprocess.CalledProcessError: Command '['/content/kenlm/build/bin/build_binary', '-a', '255', '-q', '8', '-v', 'trie', '/content/lm_filtered.arpa', '/content/lm.binary']' returned non-zero exit status 1.

!python /content/DeepSpeech/data/lm/generate_lm.py \
--input_txt /content/transcripts.txt \
--output_dir /content/scorer/ \
--top_k 50000 \
--kenlm_bins /content/kenlm/build/bin/ \
--arpa_order 5 --max_arpa_memory "95%" --arpa_prune "0|0|1" \
--binary_a_bits 255 --binary_q_bits 8 --binary_type trie

%cd /content/DeepSpeech
!python3 DeepSpeech.py --train_cudnn True --early_stop True --es_epochs 6 --n_hidden 2048 --epochs 20 \
  --export_dir /content/models/ --checkpoint_dir /content/model_checkpoints/ \
  --train_files /content/train.csv --dev_files /content/dev.csv --test_files /content/test.csv \
  --learning_rate 0.0001 --train_batch_size 64 --test_batch_size 32 --dev_batch_size 32 --export_file_name 'ft_model' \
   --augment reverb[p=0.2,delay=50.0~30.0,decay=10.0:2.0~1.0] \
   --augment volume[p=0.2,dbfs=-10:-40] \
   --augment pitch[p=0.2,pitch=1~0.2] \
   --augment tempo[p=0.2,factor=1~0.5] 

 Future _loadModel() async {
    final bytes =
        await rootBundle.load('assets/deepspeech-0.9.3-models.tflite');
    final directory = (await getApplicationDocumentsDirectory()).path;

The method 'getApplicationDocumentsDirectory' isn't defined for the type '_MyAppState'.
Try correcting the name to the name of an existing method, or defining a method named 'getApplicationDocumentsDirectory'

!wget https://github.com/mozilla/DeepSpeech/releases/download/v0.9.3/ds_ctcdecoder-0.9.3-cp36-cp36m-manylinux1_x86_64.whl

!pip install /content/~path~/ds_ctcdecoder-0.9.3-cp36-cp36m-manylinux1_x86_64.whl

{'index': 1, 'structVersion': 2, 'name': 'Logitech USB Microphone: Audio (hw:1,0)', 'hostApi': 0, 'maxInputChannels': 1, 'maxOutputChannels': 0, 'defaultLowInputLatency': 0.008684807256235827, 'defaultLowOutputLatency': -1.0, 'defaultHighInputLatency': 0.034829931972789115, 'defaultHighOutputLatency': -1.0, 'defaultSampleRate': 44100.0}

pcm.!default {
        type asymd
        playback.pcm
        {
                type plug
                slave.pcm "dmix"
        }
        capture.pcm
        {
                type plug
                slave.pcm "usb"
        }
}

ctl.!default {
        type hw
        card 0
}

pcm.usb {
        type hw
        card 1
        device 0
        rate 16000
} 

self.paStream = self.pa.open(rate = self.model.sampleRate(), channels = 1, format= pyaudio.paInt16, input=True, input_device_index = 1, frames_per_buffer= self.model.beamWidth())

def __get_next_audio_frame__(self):
    audio_frame = self.paStream.read(self.model.beamWidth(), exception_on_overflow= False)  
    audio_frame = struct.unpack_from("h" * self.model.beamWidth(), audio_frame)     
    return audio_frame

modelStream.feedAudioContent(self.__get_next_audio_frame__())