148 lines
6.8 KiB
Bash
Executable File
148 lines
6.8 KiB
Bash
Executable File
#!/usr/bin/env bash
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# Copyright 2024 Xiaomi Corp. (authors: Fangjun Kuang)
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set -ex
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log() {
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# This function is from espnet
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local fname=${BASH_SOURCE[1]##*/}
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echo -e "$(date '+%Y-%m-%d %H:%M:%S') (${fname}:${BASH_LINENO[0]}:${FUNCNAME[1]}) $*"
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}
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# 36000 hours of English data
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url=https://catalog.ngc.nvidia.com/orgs/nvidia/teams/nemo/models/parakeet-tdt_ctc-110m
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name=$(basename $url)
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doc="parakeet-tdt_ctc-110m is an ASR model that transcribes speech with Punctuations and Capitalizations of the English alphabet. It was trained on 36K hours of English speech collected and prepared by NVIDIA NeMo and Suno teams."
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log "Process $name at $url"
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./export-onnx-transducer-non-streaming.py --model $name --doc "$doc"
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d=sherpa-onnx-nemo-parakeet_tdt_transducer_110m-en-36000
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mkdir -p $d
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mv -v *.onnx $d/
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mv -v tokens.txt $d/
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ls -lh $d
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# 8500 hours of English speech
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url=https://catalog.ngc.nvidia.com/orgs/nvidia/teams/nemo/models/stt_en_fastconformer_hybrid_large_pc
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name=$(basename $url)
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doc="This collection contains the English FastConformer Hybrid (Transducer and CTC) Large model (around 114M parameters) with Punctuation and Capitalization on NeMo ASRSet En PC with around 8500 hours of English speech (SPGI 1k, VoxPopuli, MCV11, Europarl-ASR, Fisher, LibriSpeech, NSC1, MLS). It utilizes a Google SentencePiece [1] tokenizer with a vocabulary size of 1024. It transcribes text in upper and lower case English alphabet along with spaces, periods, commas, question marks, and a few other characters."
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log "Process $name at $url"
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./export-onnx-transducer-non-streaming.py --model $name --doc "$doc"
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d=sherpa-onnx-nemo-fast-conformer-transducer-en-24500
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mkdir -p $d
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mv -v *.onnx $d/
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mv -v tokens.txt $d/
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ls -lh $d
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url=https://catalog.ngc.nvidia.com/orgs/nvidia/teams/nemo/models/stt_es_fastconformer_hybrid_large_pc
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name=$(basename $url)
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doc="This collection contains the Spanish FastConformer Hybrid (CTC and Transducer) Large model (around 114M parameters) with Punctuation and Capitalization. It is trained on the NeMo PnC ES ASRSET (Fisher, MCV12, MLS, Voxpopuli) containing 1424 hours of Spanish speech. It utilizes a Google SentencePiece [1] tokenizer with vocabulary size 1024, and transcribes text in upper and lower case Spanish alphabet along with spaces, period, comma, question mark and inverted question mark."
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./export-onnx-transducer-non-streaming.py --model $name --doc "$doc"
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d=sherpa-onnx-nemo-fast-conformer-transducer-es-1424
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mkdir -p $d
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mv -v *.onnx $d/
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mv -v tokens.txt $d/
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ls -lh $d
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url=https://catalog.ngc.nvidia.com/orgs/nvidia/teams/nemo/models/stt_multilingual_fastconformer_hybrid_large_pc_blend_eu
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name=$(basename $url)
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doc="This collection contains the Multilingual FastConformer Hybrid (Transducer and CTC) Large model (around 114M parameters) with Punctuation and Capitalization. It is trained on the NeMo PnC German, English, Spanish, and French ASR sets that contain 14,288 hours of speech in total. It utilizes a Google SentencePiece [1] tokenizer with vocabulary size 256 per language and transcribes text in upper and lower case along with spaces, periods, commas, question marks and a few other language-specific characters. The total tokenizer size is 2560, of which 1024 tokens are allocated to English, German, French, and Spanish. The remaining tokens are reserved for future languages."
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./export-onnx-transducer-non-streaming.py --model $name --doc "$doc"
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d=sherpa-onnx-nemo-fast-conformer-transducer-en-de-es-fr-14288
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mkdir -p $d
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mv -v *.onnx $d/
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mv -v tokens.txt $d/
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ls -lh $d
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url=https://catalog.ngc.nvidia.com/orgs/nvidia/teams/nemo/models/stt_multilingual_fastconformer_hybrid_large_pc
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name=$(basename $url)
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doc="This collection contains the Multilingual FastConformer Hybrid (Transducer and CTC) Large model (around 114M parameters) with Punctuation and Capitalization. It is trained on the NeMo PnC Belarusian, German, English, Spanish, French, Croatian, Italian, Polish, Russian, and Ukrainian ASR sets that contain ~20,000 hours of speech in total. It utilizes a Google SentencePiece [1] tokenizer with vocabulary size 256 per language (2560 total), and transcribes text in upper and lower case along with spaces, periods, commas, question marks and a few other language-specific characters."
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./export-onnx-transducer-non-streaming.py --model $name --doc "$doc"
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d=sherpa-onnx-nemo-fast-conformer-transducer-be-de-en-es-fr-hr-it-pl-ru-uk-20k
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mkdir -p $d
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mv -v *.onnx $d/
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mv -v tokens.txt $d/
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ls -lh $d
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# Now test the exported model
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log "Download test data"
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curl -SL -O https://github.com/k2-fsa/sherpa-onnx/releases/download/asr-models/spoken-language-identification-test-wavs.tar.bz2
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tar xvf spoken-language-identification-test-wavs.tar.bz2
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rm spoken-language-identification-test-wavs.tar.bz2
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data=spoken-language-identification-test-wavs
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curl -SL -O https://dldata-public.s3.us-east-2.amazonaws.com/2086-149220-0033.wav
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mv 2086-149220-0033.wav en.wav
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d=sherpa-onnx-nemo-parakeet_tdt_transducer_110m-en-36000
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python3 ./test-onnx-transducer-non-streaming.py \
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--encoder $d/encoder.onnx \
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--decoder $d/decoder.onnx \
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--joiner $d/joiner.onnx \
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--tokens $d/tokens.txt \
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--wav $data/en-english.wav
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python3 ./test-onnx-transducer-non-streaming.py \
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--encoder $d/encoder.onnx \
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--decoder $d/decoder.onnx \
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--joiner $d/joiner.onnx \
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--tokens $d/tokens.txt \
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--wav ./en.wav
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mkdir -p $d/test_wavs
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cp en.wav $d/test_wavs/0.wav
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cp -v $data/en-english.wav $d/test_wavs
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d=sherpa-onnx-nemo-fast-conformer-transducer-en-24500
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python3 ./test-onnx-transducer-non-streaming.py \
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--encoder $d/encoder.onnx \
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--decoder $d/decoder.onnx \
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--joiner $d/joiner.onnx \
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--tokens $d/tokens.txt \
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--wav $data/en-english.wav
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mkdir -p $d/test_wavs
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cp en.wav $d/test_wavs/0.wav
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cp -v $data/en-english.wav $d/test_wavs
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d=sherpa-onnx-nemo-fast-conformer-transducer-es-1424
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python3 ./test-onnx-transducer-non-streaming.py \
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--encoder $d/encoder.onnx \
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--decoder $d/decoder.onnx \
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--joiner $d/joiner.onnx \
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--tokens $d/tokens.txt \
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--wav $data/es-spanish.wav
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mkdir -p $d/test_wavs
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cp -v $data/es-spanish.wav $d/test_wavs
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d=sherpa-onnx-nemo-fast-conformer-transducer-en-de-es-fr-14288
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mkdir -p $d/test_wavs
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for w in en-english.wav de-german.wav es-spanish.wav fr-french.wav; do
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python3 ./test-onnx-transducer-non-streaming.py \
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--encoder $d/encoder.onnx \
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--decoder $d/decoder.onnx \
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--joiner $d/joiner.onnx \
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--tokens $d/tokens.txt \
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--wav $data/$w
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cp -v $data/$w $d/test_wavs
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done
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d=sherpa-onnx-nemo-fast-conformer-transducer-be-de-en-es-fr-hr-it-pl-ru-uk-20k
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mkdir -p $d/test_wavs
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for w in en-english.wav de-german.wav es-spanish.wav fr-french.wav hr-croatian.wav it-italian.wav po-polish.wav ru-russian.wav uk-ukrainian.wav; do
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python3 ./test-onnx-transducer-non-streaming.py \
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--encoder $d/encoder.onnx \
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--decoder $d/decoder.onnx \
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--joiner $d/joiner.onnx \
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--tokens $d/tokens.txt \
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--wav $data/$w
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cp -v $data/$w $d/test_wavs
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done
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