u2 kaldi mutli process test with batchsize one

deepspeech/exps/u2/model.py

@@ -444,7 +444,7 @@ class U2Tester(U2Trainer):
         start_time = time.time()
         text_feature = self.test_loader.collate_fn.text_feature
         target_transcripts = self.ordid2token(texts, texts_len)
-        result_transcripts = self.model.decode(
+        result_transcripts, result_tokenids = self.model.decode(
             audio,
             audio_len,
             text_feature=text_feature,
@@ -462,14 +462,19 @@ class U2Tester(U2Trainer):
             simulate_streaming=cfg.simulate_streaming)
         decode_time = time.time() - start_time
 
-        for utt, target, result in zip(utts, target_transcripts,
-                                       result_transcripts):
+        for utt, target, result, rec_tids in zip(
+                utts, target_transcripts, result_transcripts, result_tokenids):
             errors, len_ref = errors_func(target, result)
             errors_sum += errors
             len_refs += len_ref
             num_ins += 1
             if fout:
-                fout.write({"utt": utt, "ref": target, "hyp": result})
+                fout.write({
+                    "utt": utt,
+                    "refs": [target],
+                    "hyps": [result],
+                    "hyps_tokenid": [rec_tids],
+                })
             logger.info(f"Utt: {utt}")
             logger.info(f"Ref: {target}")
             logger.info(f"Hyp: {result}")

deepspeech/exps/u2_kaldi/model.py

@@ -390,6 +390,10 @@ class U2Tester(U2Trainer):
 
     def __init__(self, config, args):
         super().__init__(config, args)
+        self.text_feature = TextFeaturizer(
+            unit_type=self.config.collator.unit_type,
+            vocab_filepath=self.config.collator.vocab_filepath,
+            spm_model_prefix=self.config.collator.spm_model_prefix)
 
     def id2token(self, texts, texts_len, text_feature):
         """ ord() id to chr() chr """
@@ -413,15 +417,11 @@ class U2Tester(U2Trainer):
         error_rate_func = error_rate.cer if cfg.error_rate_type == 'cer' else error_rate.wer
 
         start_time = time.time()
-        text_feature = TextFeaturizer(
-            unit_type=self.config.collator.unit_type,
-            vocab_filepath=self.config.collator.vocab_filepath,
-            spm_model_prefix=self.config.collator.spm_model_prefix)
-        target_transcripts = self.id2token(texts, texts_len, text_feature)
-        result_transcripts = self.model.decode(
+        target_transcripts = self.id2token(texts, texts_len, self.text_feature)
+        result_transcripts, result_tokenids = self.model.decode(
             audio,
             audio_len,
-            text_feature=text_feature,
+            text_feature=self.text_feature,
             decoding_method=cfg.decoding_method,
             lang_model_path=cfg.lang_model_path,
             beam_alpha=cfg.alpha,
@@ -436,14 +436,19 @@ class U2Tester(U2Trainer):
             simulate_streaming=cfg.simulate_streaming)
         decode_time = time.time() - start_time
 
-        for utt, target, result in zip(utts, target_transcripts,
-                                       result_transcripts):
+        for i, (utt, target, result, rec_tids) in enumerate(zip(
+                utts, target_transcripts, result_transcripts, result_tokenids)):
             errors, len_ref = errors_func(target, result)
             errors_sum += errors
             len_refs += len_ref
             num_ins += 1
             if fout:
-                fout.write({"utt": utt, "ref": target, "hyp": result})
+                fout.write({
+                    "utt": utt,
+                    "refs": [target],
+                    "hyps": [result],
+                    "hyps_tokenid": [rec_tids],
+                })
             logger.info(f"Utt: {utt}")
             logger.info(f"Ref: {target}")
             logger.info(f"Hyp: {result}")

deepspeech/io/collator.py

@@ -32,7 +32,7 @@ __all__ = ["SpeechCollator", "TripletSpeechCollator"]
 logger = Log(__name__).getlog()
 
 
-def tokenids(text, keep_transcription_text):
+def _tokenids(text, keep_transcription_text):
     # for training text is token ids 
     tokens = text  # token ids
 
@@ -93,6 +93,8 @@ class SpeechCollatorBase():
         a user-defined shape) within one batch.
         """
         self.keep_transcription_text = keep_transcription_text
+        self.train_mode = not keep_transcription_text
+
         self.stride_ms = stride_ms
         self.window_ms = window_ms
         self.feat_dim = feat_dim
@@ -192,6 +194,7 @@ class SpeechCollatorBase():
         texts = []
         text_lens = []
         utts = []
+        tids = [] # tokenids
 
         for idx, item in enumerate(batch):
             utts.append(item['utt'])
@@ -203,7 +206,7 @@ class SpeechCollatorBase():
             audios.append(audio)  # [T, D]
             audio_lens.append(audio.shape[0])
 
-            tokens = tokenids(text, self.keep_transcription_text)
+            tokens = _tokenids(text, self.keep_transcription_text)
             texts.append(tokens)
             text_lens.append(tokens.shape[0])
 

deepspeech/io/dataloader.py

@@ -142,6 +142,15 @@ class BatchDataLoader():
             collate_fn=batch_collate,
             num_workers=self.n_iter_processes, )
 
+    def __len__(self):
+        return len(self.dataloader)
+
+    def __iter__(self):
+        return self.dataloader.__iter__()
+
+    def __call__(self):
+        return self.__iter__()
+
     def __repr__(self):
         echo = f"<{self.__class__.__module__}.{self.__class__.__name__} object at {hex(id(self))}> "
         echo += f"train_mode: {self.train_mode}, "
@@ -159,12 +168,3 @@ class BatchDataLoader():
         echo += f"num_workers: {self.n_iter_processes}, "
         echo += f"file: {self.json_file}"
         return echo
-
-    def __len__(self):
-        return len(self.dataloader)
-
-    def __iter__(self):
-        return self.dataloader.__iter__()
-
-    def __call__(self):
-        return self.__iter__()

deepspeech/models/u2/u2.py

@@ -809,7 +809,8 @@ class U2BaseModel(nn.Layer):
             raise ValueError(f"Not support decoding method: {decoding_method}")
 
         res = [text_feature.defeaturize(hyp) for hyp in hyps]
-        return res
+        res_tokenids = [hyp for hyp in hyps]
+        return res, res_tokenids
 
 
 class U2Model(U2BaseModel):

examples/librispeech/s1/conf/augmentation.json

 [
-  {
-    "type": "shift",
-    "params": {
-      "min_shift_ms": -5,
-      "max_shift_ms": 5
-    },
-    "prob": 1.0
-  },
   {
     "type": "speed",
     "params": {
@@ -16,6 +8,14 @@
     },
     "prob": 0.0
   },
+  {
+    "type": "shift",
+    "params": {
+      "min_shift_ms": -5,
+      "max_shift_ms": 5
+    },
+    "prob": 1.0
+  },
   {
     "type": "specaug",
     "params": {

examples/librispeech/s2/README.md

 # LibriSpeech
 
-## Data
-| Data Subset | Duration in Seconds |
-| data/manifest.train |  0.83s ~ 29.735s |
-| data/manifest.dev | 1.065 ~ 35.155s |  
-| data/manifest.test-clean | 1.285s ~ 34.955s |
-
-## Conformer
-| Model | Params | Config | Augmentation| Test set | Decode method | Loss | WER |  
-| --- | --- | --- | --- | --- | --- | --- | --- |
-| conformer | 47.63 M | conf/conformer.yaml | spec_aug + shift | test-clean | attention | - | - |  
-| conformer | 47.63 M | conf/conformer.yaml | spec_aug + shift | test-clean | ctc_greedy_search |  |  |  
-| conformer | 47.63 M | conf/conformer.yaml | spec_aug + shift | test-clean | ctc_prefix_beam_search |  | |  
-| conformer | 47.63 M | conf/conformer.yaml | spec_aug + shift | test-clean | attention_rescoring |  |  |  
-
-### Test w/o length filter
-| Model | Params | Config | Augmentation| Test set | Decode method | Loss | WER |  
-| --- | --- | --- | --- | --- | --- | --- | --- |
-| conformer | 47.63 M | conf/conformer.yaml | spec_aug + shift | test-clean-all | attention |  |  |  
-
-
-## Chunk Conformer
-
-| Model | Params | Config | Augmentation| Test set | Decode method | Chunk Size & Left Chunks | Loss | WER |  
-| --- | --- | --- | --- | --- | --- | --- | --- | --- |  
-| conformer | 47.63 M | conf/chunk_conformer.yaml | spec_aug + shift | test-clean | attention | 16, -1 |  |  |  
-| conformer | 47.63 M | conf/chunk_conformer.yaml | spec_aug + shift | test-clean | ctc_greedy_search | 16, -1 |  |  |  
-| conformer | 47.63 M | conf/chunk_conformer.yaml | spec_aug + shift | test-clean | ctc_prefix_beam_search | 16, -1 |  | - |  
-| conformer | 47.63 M | conf/chunk_conformer.yaml | spec_aug + shift | test-clean | attention_rescoring | 16, -1 |  | - |  
-
-
 ## Transformer
-| Model | Params | Config | Augmentation| Test set | Decode method | Loss | WER |  
-| --- | --- | --- | --- | --- | --- | --- | --- |
-| transformer | 32.52 M | conf/transformer.yaml | spec_aug + shift | test-clean | attention |  |  |  
-
-### Test w/o length filter
-| Model | Params | Config | Augmentation| Test set | Decode method | Loss | WER |  
+| Model | Params | Config | Augmentation| Test Set | Decode Method | Loss | WER % |  
 | --- | --- | --- | --- | --- | --- | --- | --- |
-| transformer | 32.52 M | conf/transformer.yaml | spec_aug + shift | test-clean-all | attention | | |  
+| transformer | 32.52 M | conf/transformer.yaml | spec_aug | test-clean | attention | 6.395054340362549 | 4.2 |  
+| transformer | 32.52 M | conf/transformer.yaml | spec_aug | test-clean | ctc_greedy_search | 6.395054340362549 | 5.0 |  
+| transformer | 32.52 M | conf/transformer.yaml | spec_aug | test-clean | ctc_prefix_beam_search | 6.395054340362549 |  |  
+| transformer | 32.52 M | conf/transformer.yaml | spec_aug | test-clean | attention_rescore | 6.395054340362549 |  |  

examples/librispeech/s2/conf/transformer.yaml

@@ -5,9 +5,9 @@ data:
   test_manifest: data/manifest.test-clean
 
 collator:
-  vocab_filepath: data/train_960_unigram5000_units.txt
-  unit_type: 'spm'
-  spm_model_prefix: 'data/train_960_unigram5000'
+  vocab_filepath: data/bpe_unigram_5000_units.txt
+  unit_type: spm
+  spm_model_prefix: data/bpe_unigram_5000
   feat_dim: 83
   stride_ms: 10.0
   window_ms: 25.0

examples/librispeech/s2/local/test.sh

@@ -46,15 +46,17 @@ pids=() # initialize pids
 
 for dmethd in attention ctc_greedy_search ctc_prefix_beam_search attention_rescoring; do
 (
+    echo "${dmethd} decoding"
     for rtask in ${recog_set}; do
     (
-        decode_dir=decode_${rtask}_${dmethd}_$(basename ${config_path%.*})_${lmtag}
+        echo "${rtask} dataset"
+        decode_dir=decode_${rtask/-/_}_${dmethd}_$(basename ${config_path%.*})_${lmtag}
         feat_recog_dir=${datadir}
         mkdir -p ${expdir}/${decode_dir}
         mkdir -p ${feat_recog_dir}
 
         # split data
-        split_json.sh ${feat_recog_dir}/manifest.${rtask} ${nj}
+        split_json.sh manifest.${rtask} ${nj}
 
         #### use CPU for decoding
         ngpu=0
@@ -74,17 +76,16 @@ for dmethd in attention ctc_greedy_search ctc_prefix_beam_search attention_resco
             --opts decoding.batch_size ${batch_size} \
             --opts data.test_manifest ${feat_recog_dir}/split${nj}/JOB/manifest.${rtask}
 
-        score_sclite.sh --bpe ${nbpe} --bpemodel ${bpemodel}.model --wer true ${expdir}/${decode_dir} ${dict}
+        score_sclite.sh --bpe ${nbpe} --bpemodel ${bpemodel} --wer false ${expdir}/${decode_dir} ${dict}
 
     ) &
     pids+=($!) # store background pids
+    i=0; for pid in "${pids[@]}"; do wait ${pid} || ((++i)); done
+    [ ${i} -gt 0 ] && echo "$0: ${i} background jobs are failed." && false
     done
-) &
-pids+=($!) # store background pids
+)
 done
 
-i=0; for pid in "${pids[@]}"; do wait ${pid} || ((++i)); done
-[ ${i} -gt 0 ] && echo "$0: ${i} background jobs are failed." && false
 echo "Finished"
 
 exit 0

examples/librispeech/s2/run.sh

@@ -32,7 +32,7 @@ fi
 
 if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
     # test ckpt avg_n
-    CUDA_VISIBLE_DEVICES=0 ./local/test.sh ${conf_path} ${dict_path} exp/${ckpt}/checkpoints/${avg_ckpt} || exit -1
+    ./local/test.sh ${conf_path} ${dict_path} exp/${ckpt}/checkpoints/${avg_ckpt} || exit -1
 fi
 
 if [ ${stage} -le 4 ] && [ ${stop_stage} -ge 4 ]; then

tools/Makefile

@@ -6,7 +6,7 @@ CC ?= gcc        # used for sph2pipe
 # CXX = clang++  # Uncomment these lines...
 # CC = clang     # ...to build with Clang.
 
-WGET ?= wget
+WGET ?= wget --no-check-certificate
 
 .PHONY: all clean
 

utils/json2trn.py

+#!/usr/bin/env python3
+# encoding: utf-8
+# Copyright 2017 Johns Hopkins University (Shinji Watanabe)
+#           2018 Xuankai Chang (Shanghai Jiao Tong University)
+#  Apache 2.0  (http://www.apache.org/licenses/LICENSE-2.0)
+import argparse
+import json
+import logging
+import sys
+
+import jsonlines
+from utility import get_commandline_args
+
+
+def get_parser():
+    parser = argparse.ArgumentParser(
+        description="convert a json to a transcription file with a token dictionary",
+        formatter_class=argparse.ArgumentDefaultsHelpFormatter, )
+    parser.add_argument("json", type=str, help="jsonlines files")
+    parser.add_argument("dict", type=str, help="dict, not used.")
+    parser.add_argument(
+        "--num-spkrs", type=int, default=1, help="number of speakers")
+    parser.add_argument(
+        "--refs", type=str, nargs="+", help="ref for all speakers")
+    parser.add_argument(
+        "--hyps", type=str, nargs="+", help="hyp for all outputs")
+    return parser
+
+
+def main(args):
+    args = get_parser().parse_args(args)
+    convert(args.json, args.dict, args.refs, args.hyps, args.num_spkrs)
+
+
+def convert(jsonf, dic, refs, hyps, num_spkrs=1):
+    n_ref = len(refs)
+    n_hyp = len(hyps)
+    assert n_ref == n_hyp
+    assert n_ref == num_spkrs
+
+    # logging info
+    logfmt = "%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s"
+    logging.basicConfig(level=logging.INFO, format=logfmt)
+    logging.info(get_commandline_args())
+
+    logging.info("reading %s", jsonf)
+    with jsonlines.open(jsonf, "r") as f:
+        j = [item for item in f]
+
+    logging.info("reading %s", dic)
+    with open(dic, "r") as f:
+        dictionary = f.readlines()
+    char_list = [entry.split(" ")[0] for entry in dictionary]
+    char_list.insert(0, "<blank>")
+    char_list.append("<eos>")
+
+    for ns in range(num_spkrs):
+        hyp_file = open(hyps[ns], "w")
+        ref_file = open(refs[ns], "w")
+
+        for x in j:
+            # recognition hypothesis
+            if num_spkrs == 1:
+                #seq = [char_list[int(i)] for i in x['hyps_tokenid'][0]]
+                seq = x['hyps'][0]
+            else:
+                seq = [char_list[int(i)] for i in x['hyps_tokenid'][ns]]
+            # In the recognition hypothesis,
+            # the <eos> symbol is usually attached in the last part of the sentence
+            # and it is removed below.
+            #hyp_file.write(" ".join(seq).replace("<eos>", ""))
+            hyp_file.write(seq.replace("<eos>", ""))
+            # spk-uttid
+            hyp_file.write(" (" + x["utt"] + ")\n")
+
+            # reference
+            if num_spkrs == 1:
+                seq = x["refs"][0]
+            else:
+                seq = x['refs'][ns]
+            # Unlike the recognition hypothesis,
+            # the reference is directly generated from a token without dictionary
+            # to avoid to include <unk> symbols in the reference to make scoring normal.
+            # The detailed discussion can be found at
+            # https://github.com/espnet/espnet/issues/993
+            # ref_file.write(
+            #     seq + " (" + j["utts"][x]["utt2spk"].replace("-", "_") + "-" + x + ")\n"
+            # )
+            ref_file.write(seq + " (" + x['utt'] + ")\n")
+
+        hyp_file.close()
+        ref_file.close()
+
+
+if __name__ == "__main__":
+    main(sys.argv[1:])

utils/score_sclite.sh

 #!/usr/bin/env bash
 
+set -e
+
 # Copyright 2017 Johns Hopkins University (Shinji Watanabe)
 #  Apache 2.0  (http://www.apache.org/licenses/LICENSE-2.0)
 

utils/utility.py

@@ -14,6 +14,7 @@
 import hashlib
 import json
 import os
+import sys
 import tarfile
 import zipfile
 from typing import Text
@@ -21,7 +22,7 @@ from typing import Text
 __all__ = [
     "check_md5sum", "getfile_insensitive", "download_multi", "download",
     "unpack", "unzip", "md5file", "print_arguments", "add_arguments",
-    "read_manifest"
+    "read_manifest", "get_commandline_args"
 ]
 
 
@@ -46,6 +47,40 @@ def read_manifest(manifest_path):
     return manifest
 
 
+def get_commandline_args():
+    extra_chars = [
+        " ",
+        ";",
+        "&",
+        "(",
+        ")",
+        "|",
+        "^",
+        "<",
+        ">",
+        "?",
+        "*",
+        "[",
+        "]",
+        "$",
+        "`",
+        '"',
+        "\\",
+        "!",
+        "{",
+        "}",
+    ]
+
+    # Escape the extra characters for shell
+    argv = [
+        arg.replace("'", "'\\''") if all(char not in arg
+                                         for char in extra_chars) else
+        "'" + arg.replace("'", "'\\''") + "'" for arg in sys.argv
+    ]
+
+    return sys.executable + " " + " ".join(argv)
+
+
 def print_arguments(args, info=None):
     """Print argparse's arguments.