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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,84 @@ | ||
| import argparse | ||
| import json | ||
| import sys | ||
| from multiprocessing.pool import Pool | ||
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| import numpy as np | ||
| import torch | ||
| from tqdm import tqdm | ||
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| from decoder import BeamCTCDecoder | ||
| from model import DeepSpeech | ||
| from opts import add_decoder_args | ||
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| parser = argparse.ArgumentParser(description='Tune an ARPA LM based on a pre-trained acoustic model output') | ||
| parser.add_argument('--model-path', default='models/deepspeech_final.pth', | ||
| help='Path to model file created by training') | ||
| parser.add_argument('--saved-output', default="", type=str, help='Path to output from test.py') | ||
| parser.add_argument('--num-workers', default=16, type=int, help='Number of parallel decodes to run') | ||
| parser.add_argument('--output-path', default="tune_results.json", help="Where to save tuning results") | ||
| parser.add_argument('--lm-alpha-from', default=0.0, type=float, help='Language model weight start tuning') | ||
| parser.add_argument('--lm-alpha-to', default=3.0, type=float, help='Language model weight end tuning') | ||
| parser.add_argument('--lm-beta-from', default=0.0, type=float, | ||
| help='Language model word bonus (all words) start tuning') | ||
| parser.add_argument('--lm-beta-to', default=0.5, type=float, | ||
| help='Language model word bonus (all words) end tuning') | ||
| parser.add_argument('--lm-num-alphas', default=45, type=float, help='Number of alpha candidates for tuning') | ||
| parser.add_argument('--lm-num-betas', default=8, type=float, help='Number of beta candidates for tuning') | ||
| parser = add_decoder_args(parser) | ||
| args = parser.parse_args() | ||
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| if args.lm_path is None: | ||
| print("error: LM must be provided for tuning") | ||
| sys.exit(1) | ||
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| model = DeepSpeech.load_model(args.model_path) | ||
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| saved_output = np.load(args.saved_output) | ||
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| def init(beam_width, blank_index, lm_path): | ||
| global decoder | ||
| decoder = BeamCTCDecoder(model.labels, lm_path=lm_path, beam_width=beam_width, num_processes=args.lm_workers, | ||
| blank_index=blank_index) | ||
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| def decode_dataset(params): | ||
| lm_alpha, lm_beta = params | ||
| global decoder | ||
| decoder._decoder.reset_params(lm_alpha, lm_beta) | ||
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| total_cer, total_wer, num_tokens, num_chars = 0, 0, 0, 0 | ||
| for out, sizes, target_strings in saved_output: | ||
| out = torch.Tensor(out).float() | ||
| sizes = torch.Tensor(sizes).int() | ||
| decoded_output, _, = decoder.decode(out, sizes) | ||
| for x in range(len(target_strings)): | ||
| transcript, reference = decoded_output[x][0], target_strings[x][0] | ||
| wer_inst = decoder.wer(transcript, reference) | ||
| cer_inst = decoder.cer(transcript, reference) | ||
| total_cer += cer_inst | ||
| total_wer += wer_inst | ||
| num_tokens += len(reference.split()) | ||
| num_chars += len(reference) | ||
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| wer = float(total_wer) / num_tokens | ||
| cer = float(total_cer) / num_chars | ||
|
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| return [lm_alpha, lm_beta, wer * 100, cer * 100] | ||
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| if __name__ == '__main__': | ||
| p = Pool(args.num_workers, init, [args.beam_width, model.labels.index('_'), args.lm_path]) | ||
|
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| cand_alphas = np.linspace(args.lm_alpha_from, args.lm_alpha_to, args.lm_num_alphas) | ||
| cand_betas = np.linspace(args.lm_beta_from, args.lm_beta_to, args.lm_num_betas) | ||
| params_grid = [(float(alpha), float(beta)) for alpha in cand_alphas | ||
| for beta in cand_betas] | ||
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| scores = [] | ||
| for params in tqdm(p.imap(decode_dataset, params_grid), total=len(params_grid)): | ||
| scores.append(list(params)) | ||
| print("Saving tuning results to: {}".format(args.output_path)) | ||
| with open(args.output_path, "w") as fh: | ||
| json.dump(scores, fh) |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,12 @@ | ||
| import argparse | ||
| import json | ||
|
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| parser = argparse.ArgumentParser(description='Select the best parameters based on the WER') | ||
| parser.add_argument('--input-path', type=str, help='Output json file from search_lm_params') | ||
| args = parser.parse_args() | ||
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| with open(args.input_path) as f: | ||
| results = json.load(f) | ||
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| min_results = min(results, key=lambda x: x[2]) # Find the minimum WER (alpha, beta, WER, CER) | ||
| print("Alpha: %f \nBeta: %f \nWER: %f\nCER: %f" % tuple(min_results)) |
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