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pplantingaAdel-Moumen
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Recipe test fixes (#2874)
Co-authored-by: Adel Moumen <adelmoumen.pro@gmail.com>
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-18
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-18
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‎.github/workflows/pythonapp.yml‎

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@@ -30,7 +30,7 @@ jobs:
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run: |
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pip install uv
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uv pip install --system ctc-segmentation sacrebleu # ctc-segmentation is funky with uv due to their oldest-supported-numpy dependency
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uv pip install --system -r requirements.txt torch==2.6.0+cpu torchaudio==2.6.0+cpu --extra-index-url https://download.pytorch.org/whl/cpu k2==1.24.4.dev20250307+cpu.torch2.6.0 --find-links https://k2-fsa.github.io/k2/cpu.html gensim==4.3.2 bitsandbytes==0.45.3 scikit-learn==1.6.1
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uv pip install --system -r requirements.txt torch==2.6.0+cpu torchaudio==2.6.0+cpu --extra-index-url https://download.pytorch.org/whl/cpu k2==1.24.4.dev20250307+cpu.torch2.6.0 --find-links https://k2-fsa.github.io/k2/cpu.html gensim==4.3.2 bitsandbytes==0.45.3 scikit-learn==1.6.1 accelerate
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uv pip install --system --editable . --no-deps # already installed pinned deps from requirements.txt, we're good
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- name: Install sox
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run: |

‎recipes/LJSpeech/quantization/train.py‎

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@@ -142,7 +142,7 @@ def dataio_prepare(hparams):
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"""
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train_data = sb.dataio.dataset.DynamicItemDataset.from_json(
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json_path=hparams["train_json"],
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replacements={"DATA_ROOT": hparams["data_folder"]},
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replacements={"data_root": hparams["data_folder"]},
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)
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# Sort training data to speed up training
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train_data = train_data.filtered_sorted(
@@ -153,7 +153,7 @@ def dataio_prepare(hparams):
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valid_data = sb.dataio.dataset.DynamicItemDataset.from_json(
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json_path=hparams["valid_json"],
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replacements={"DATA_ROOT": hparams["data_folder"]},
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replacements={"data_root": hparams["data_folder"]},
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)
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# Sort validation data to speed up validation
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valid_data = valid_data.filtered_sorted(
@@ -164,7 +164,7 @@ def dataio_prepare(hparams):
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test_data = sb.dataio.dataset.DynamicItemDataset.from_json(
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json_path=hparams["test_json"],
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replacements={"DATA_ROOT": hparams["data_folder"]},
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replacements={"data_root": hparams["data_folder"]},
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)
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# Sort the test data to speed up testing
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test_data = test_data.filtered_sorted(

‎recipes/Voicebank/dereverb/MetricGAN-U/train.py‎

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@@ -401,7 +401,7 @@ def write_wavs(self, batch_id, wavs, score, lens):
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for i, (name, pred_wav, length) in enumerate(zip(batch_id, wavs, lens)):
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path = os.path.join(self.hparams.MetricGAN_folder, name + ".wav")
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data = torch.unsqueeze(pred_wav[: int(length)].cpu(), 0)
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torchaudio.save(path, data, self.hparams.Sample_rate)
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torchaudio.save(path, data.detach(), self.hparams.Sample_rate)
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# Make record of path and score for historical training
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score = float(score[i][0])

‎recipes/Voicebank/enhance/MetricGAN-U/train.py‎

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Original file line numberDiff line numberDiff line change
@@ -394,7 +394,7 @@ def write_wavs(self, batch_id, wavs, score, lens):
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for i, (name, pred_wav, length) in enumerate(zip(batch_id, wavs, lens)):
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path = os.path.join(self.hparams.MetricGAN_folder, name + ".wav")
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data = torch.unsqueeze(pred_wav[: int(length)].cpu(), 0)
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torchaudio.save(path, data, self.hparams.Sample_rate)
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torchaudio.save(path, data.detach(), self.hparams.Sample_rate)
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# Make record of path and score for historical training
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score = float(score[i][0])

‎recipes/Voicebank/enhance/MetricGAN/train.py‎

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@@ -280,7 +280,7 @@ def write_wavs(self, batch_id, wavs, clean_paths, scores, lens):
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):
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path = os.path.join(self.hparams.MetricGAN_folder, name + ".wav")
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data = torch.unsqueeze(pred_wav[: int(length)].cpu(), 0)
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torchaudio.save(path, data, self.hparams.Sample_rate)
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torchaudio.save(path, data.detach(), self.hparams.Sample_rate)
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# Make record of path and score for historical training
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score = float(scores[i][0])

‎recipes/timers-and-such/decoupled/hparams/train_TAS_LM.yaml‎

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Original file line numberDiff line numberDiff line change
@@ -79,9 +79,9 @@ asr_model: !apply:speechbrain.inference.ASR.EncoderDecoderASR.from_hparams
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embedding_dim: 128
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dropout: 0.
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rnn_layers: 2
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rnn_neurons: 2048
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rnn_neurons: 1024
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dnn_blocks: 1
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dnn_neurons: 512
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dnn_neurons: 1024
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return_hidden: True
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pretrainer:
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paths:

‎recipes/timers-and-such/multistage/hparams/train_TAS_LM.yaml‎

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@@ -137,9 +137,9 @@ asr_model: !apply:speechbrain.inference.ASR.EncoderDecoderASR.from_hparams
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embedding_dim: 128
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dropout: 0.
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rnn_layers: 2
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rnn_neurons: 2048
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rnn_neurons: 1024
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dnn_blocks: 1
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dnn_neurons: 512
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dnn_neurons: 1024
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return_hidden: True
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pretrainer:
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paths:

‎tests/recipes/GigaSpeech.csv‎

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@@ -1,3 +1,3 @@
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Task,Dataset,Script_file,Hparam_file,Data_prep_file,Readme_file,Result_url,HF_repo,test_debug_flags,test_debug_checks,performance
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ASR-CTC,GigaSpeech,recipes/GigaSpeech/ASR/CTC/train_with_wavlm.py,recipes/GigaSpeech/ASR/CTC/hparams/train_hf_wavlm.yaml,recipes/GigaSpeech/ASR/CTC/gigaspeech_prepare.py,recipes/GigaSpeech/ASR/CTC/README.md,,,--data_folder=tests/samples/ASR/ --train_csv=tests/samples/annotation/ASR_train.csv --valid_csv=tests/samples/annotation/ASR_train.csv --test_csv=tests/samples/annotation/ASR_train.csv --number_of_epochs=1 --skip_prep=True --wav2vec2_folder=tests/tmp/wav2vec2_checkpoint,
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ASR-Transducers,GigaSpeech,recipes/GigaSpeech/ASR/transducer/train.py,recipes/GigaSpeech/ASR/transducer/hparams/conformer_transducer.yaml,recipes/GigaSpeech/ASR/transducer/gigaspeech_prepare.py,recipes/GigaSpeech/ASR/transducer/README.md,,,--data_folder=tests/samples/ASR/ --train_csv=tests/samples/annotation/ASR_train.csv --valid_csv=tests/samples/annotation/ASR_train.csv --test_csv=tests/samples/annotation/ASR_train.csv --number_of_epochs=1 --skip_prep=True,
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ASR-Transducers,GigaSpeech,recipes/GigaSpeech/ASR/transducer/train.py,recipes/GigaSpeech/ASR/transducer/hparams/conformer_transducer.yaml,recipes/GigaSpeech/ASR/transducer/gigaspeech_prepare.py,recipes/GigaSpeech/ASR/transducer/README.md,,,--data_folder=tests/samples/ASR/ --train_csv=tests/samples/annotation/ASR_train.csv --valid_csv=tests/samples/annotation/ASR_train.csv --test_csv=tests/samples/annotation/ASR_train.csv --number_of_epochs=1 --output_neurons=29 --skip_prep=True --use_torchaudio=True,

‎tests/recipes/LJSpeech.csv‎

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@@ -5,4 +5,4 @@ TTS,LJSpeech,recipes/LJSpeech/TTS/tacotron2/train.py,recipes/LJSpeech/TTS/tacotr
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TTS,LJSpeech,recipes/LJSpeech/TTS/vocoder/hifigan/train.py,recipes/LJSpeech/TTS/vocoder/hifigan/hparams/train.yaml,recipes/LJSpeech/ljspeech_prepare.py,recipes/LJSpeech/TTS/README.md,https://www.dropbox.com/sh/m2xrdssiroipn8g/AAD-TqPYLrSg6eNxUkcImeg4a?dl=0,https://huggingface.co/speechbrain/tts-hifigan-ljspeech,--epochs=2 --data_folder=tests/samples/ASR --train_json=tests/samples/annotation/ASR_train.json --valid_json=tests/samples/annotation/ASR_dev.json --test_json=tests/samples/annotation/ASR_dev.json --skip_prep=True --sample_rate=16000,"file_exists=[train_log.txt,log.txt,env.log,train.py,hyperparams.yaml,samples/1/synthesized.wav,samples/1/target.wav,samples/2/synthesized.wav,samples/2/target.wav]"
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TTS,LJSpeech,recipes/LJSpeech/TTS/vocoder/diffwave/train.py,recipes/LJSpeech/TTS/vocoder/diffwave/hparams/train.yaml,recipes/LJSpeech/ljspeech_prepare.py,recipes/LJSpeech/TTS/README.md,,,--number_of_epochs=2 --data_folder=tests/samples/ASR --train_json=tests/samples/annotation/ASR_train.json --valid_json=tests/samples/annotation/ASR_dev.json --test_json=tests/samples/annotation/ASR_dev.json --skip_prep=True --sample_rate=16000 --num_workers 0,"file_exists=[train_log.txt,log.txt,env.log,train.py,hyperparams.yaml]"
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TTS,LJSpeech,recipes/LJSpeech/TTS/vocoder/hifigan_discrete/train.py,recipes/LJSpeech/TTS/vocoder/hifigan_discrete/hparams/train.yaml,recipes/LJSpeech/ljspeech_prepare.py,recipes/LJSpeech/TTS/README.md,,,--batch_size=2 --epochs=2 --data_folder=tests/samples/TTS --train_json=tests/samples/annotation/TTS_train.json --valid_json=tests/samples/annotation/TTS_train.json --test_json=tests/samples/annotation/TTS_train.json --skip_prep=True --sample_rate=16000 --codes_save_folder=tests/samples/TTS/codes --skip_extract=True,"file_exists=[train_log.txt,log.txt,env.log,train.py,hyperparams.yaml,samples/1/synthesized.wav,samples/1/target.wav,samples/2/synthesized.wav,samples/2/target.wav]"
8-
quantization,LJSpeech,recipes/LJSpeech/quantization/train.py,recipes/LJSpeech/quantization/hparams/train_discrete_ssl.yaml,recipes/LJSpeech/quantization/ljspeech_prepare.py,recipes/LJSpeech/quantization/README.md,,https://huggingface.co/speechbrain/SSL_Quantization,--data_folder=tests/samples/TTS --train_json=tests/samples/annotation/TTS_train.json --valid_json=tests/samples/annotation/TTS_train.json --test_json=tests/samples/annotation/TTS_train.json --skip_prep=True,"file_exists=[train_log.txt,train.py,log.txt,env.log,hyperparams.yaml]"
8+
quantization,LJSpeech,recipes/LJSpeech/quantization/train.py,recipes/LJSpeech/quantization/hparams/train_discrete_ssl.yaml,recipes/LJSpeech/quantization/ljspeech_prepare.py,recipes/LJSpeech/quantization/README.md,,https://huggingface.co/speechbrain/SSL_Quantization,--data_folder=tests/samples/TTS --train_json=tests/samples/annotation/TTS_train.json --valid_json=tests/samples/annotation/TTS_train.json --test_json=tests/samples/annotation/TTS_train.json --skip_prep=True --n_clusters 10 --kmeans_batch_size 10,"file_exists=[train_log.txt,train.py,log.txt,env.log,hyperparams.yaml]"

‎tests/recipes/LibriSpeech.csv‎

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@@ -45,4 +45,4 @@ ASR-CTC,LibriSpeech,recipes/LibriSpeech/ASR/CTC/train.py,recipes/LibriSpeech/ASR
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ASR-CTC,LibriSpeech,recipes/LibriSpeech/ASR/CTC/train.py,recipes/LibriSpeech/ASR/CTC/hparams/branchformer_large.yaml,recipes/LibriSpeech/ASR/CTC/librispeech_prepare.py,recipes/LibriSpeech/ASR/CTC/README.md,,,--data_folder=tests/samples/ASR/ --train_csv=tests/samples/annotation/ASR_train.csv --valid_csv=tests/samples/annotation/ASR_train.csv --test_csv=[tests/samples/annotation/ASR_train.csv] --number_of_epochs=2 --skip_prep=True --output_neurons=21,"file_exists=[env.log,hyperparams.yaml,log.txt,train_log.txt,train.py,wer_ASR_train.txt,save/21_char.model,save/21_char.vocab]",
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ASR-CTC,LibriSpeech,recipes/LibriSpeech/ASR/CTC/train_with_wav2vec_k2.py,recipes/LibriSpeech/ASR/CTC/hparams/train_hf_wav2vec_k2.yaml,recipes/LibriSpeech/ASR/CTC/librispeech_prepare.py,recipes/LibriSpeech/ASR/CTC/README.md,,,--data_folder=tests/samples/ASR/ --skip_prep=True --train_csv=tests/samples/annotation/ASR_train.csv --valid_csv=tests/samples/annotation/ASR_train.csv --test_csv=[tests/samples/annotation/ASR_train.csv] --number_of_epochs=2,"file_exists=[metric_ASR_train/wer_HL_1best.txt,train_log.txt,log.txt,train_with_wav2vec_k2.py,env.log,hyperparams.yaml]",
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LM,LibriSpeech,recipes/LibriSpeech/LM/train_ngram.py,recipes/LibriSpeech/LM/hparams/train_ngram.yaml,recipes/LibriSpeech/LM/librispeech_prepare.py,recipes/LibriSpeech/LM/README.md,,,--data_folder=tests/samples/ASR/ --skip_prep=True --train_csv=tests/samples/annotation/ASR_train.csv,"file_exists=[env.log,hyperparams.yaml,log.txt,lang/words.txt,libri_lm_corpus.txt,train_ngram.py]",
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quantization,LibriSpeech,recipes/LibriSpeech/quantization/train.py,recipes/LibriSpeech/quantization/hparams/train_discrete_ssl.yaml,recipes/LibriSpeech/quantization/librispeech_prepare.py,recipes/LibriSpeech/quantization/README.md,,,--data_folder=tests/samples/ASR/ --train_csv=tests/samples/annotation/ASR_train.csv --valid_csv=tests/samples/annotation/ASR_train.csv --test_csv=tests/samples/annotation/ASR_train.csv --skip_prep=True,"file_exists=[train_log.txt,train.py,log.txt,env.log,hyperparams.yaml]",
48+
quantization,LibriSpeech,recipes/LibriSpeech/quantization/train.py,recipes/LibriSpeech/quantization/hparams/train_discrete_ssl.yaml,recipes/LibriSpeech/quantization/librispeech_prepare.py,recipes/LibriSpeech/quantization/README.md,,,--data_folder=tests/samples/ASR/ --train_csv=tests/samples/annotation/ASR_train.csv --valid_csv=tests/samples/annotation/ASR_train.csv --test_csv=tests/samples/annotation/ASR_train.csv --skip_prep=True --n_clusters 10 --kmeans_batch_size 10,"file_exists=[train_log.txt,train.py,log.txt,env.log,hyperparams.yaml]",

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