ラズパイ用のRespeaker 2-Mics Pi HATというサウンドカードを買いました。
↓ReSpeaker 拡張ボード サウンドカード Raspberry pi 0/3B/3B+適用 二つのマイクつき2-Mics HAT (2-mic)
このサウンドカードとRaspberry Pi (ラズパイ) があると、AlexaやGoogle Assistantのようなスマートスピーカーを自作できるらしい。
↓Raspberry Pi Zero WH GPIOピンヘッダー ハンダ付け済み Wi-Fi & Bluetooth＆ヒートシンク付
目次
Respeaker 2-Mics Pi HAT とは
Respeaker 2-Mics Pi HAT は、AIやボイスアプリケーション用に開発されたRaspberry Pi用のデュアルマイク拡張ボードです。
Amazon Alexa Voice Service、Google AssistantなどのAIスピーカー用のサービスを利用することができます。
本体には日本語の解説はありません。
こんな感じで、Raspberry Pi ZEROに直接接続して使えます。
特徴は次の通りです。
- Raspberry Piで利用可能（Zero、1 B+、2 B、3 B）
- デュアルマイク
- 二つのGROVEインタフェース : GPIOとI2Cが利用可能
- 一つのプログラム可能なユーザーボタンおよび三つのLED
- オーディオコーデック搭載
- 二種類の音声出力ソケット : 3.5 mmオーディオジャック、JST2.0 スピーカー出力
- 3 mまでの集音距離
ReSpeakerの公式サイト：https://respeaker.io/
・https://respeaker.io/2_mic_array/
- 詳細：1.Googleアシスタント：https://youtu.be/Z3gIbxnDCtIから始めましょう。 2.ボタンを使用してGoogleアシスタントを起動する方法：https://youtu.be/bZuMimwXtII。
- ReSpeaker 2-Mics Pi HATは、AIおよび音声アプリケーション用に設計されたRaspberry Pi用のデュアルマイク拡張ボードです。つまりAmazonアレクサ音声サービス、Googleアシスタントなどを統合した、より強力で柔軟な音声製品を構築できます。このボードは、低消費電力のステレオコーデックであるWM8960に基づいて開発されています。
- 特徴：Raspberry Pi互換（Pi zero、Pi 1B +、Pi 2B、Pi 3 B）。デュアルマイク；2つのグローブインターフェイス：GPIOとI2Cをサポートします；プログラム可能なボタンとLED：1つのボタンと3つのLED；オンボードのオーディオコーデック。オーディオ出力ソケットの2種類：3.5mmオーディオジャック、JST2.0スピーカー出力；遠方支援（最大3メートル）。寸法：65mm x 30mm x 15mm。
- ハードウェア概要：ボタン：GPIO17に接続されたユーザ定義のボタン。MIC_L MIC_R：左側の左側にマイクがあります。RGB LED：3つのAPA102 RGB LED、SPIインターフェースからRaspberry Pi。WM8960：低電力ステレオコーデック。Raspberry Pi 40針：RPI0、Pi 1B +、Pi2BおよびPi3Bを支持する。POWER：ReSpeaker 2-Mics Pi HATの電源に使用するマイクロUSBポート。十分な電流を供給するために、スピーカーを使用しながらボードに電力を供給します。
- I2C：I2C-1に接続されたグローブI2Cポート。GPIO12：GPIO12およびGPIO13に接続されたGroveデジタルポート。JST 2.0スピーカー出力端子：JST 2.0コネクター。 3.5mmオーディオジャック：ヘッドホンやスピーカーを接続する場合。注：この製品にはPiゼロボードとバッテリーは含まれていません。
Respeaker Pi HATドライバーのインストール、設定
Respeaker 2-Mics Pi HATのドライバーのインストール手順は、次の通りです。
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$ sudo apt-get update $ sudo apt-get upgrade $ git clone https://github.com/respeaker/seeed-voicecard.git $ cd seeed-voicecard $ sudo ./install.sh $ sudo reboot |
リブート後に、以下のコマンドで確認します。
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$ aplay -l **** ハードウェアデバイス PLAYBACK のリスト **** カード 0: seeed2micvoicec [seeed-2mic-voicecard], デバイス 0: bcm2835-i2s-wm8960-hifi wm8960-hifi-0 [] サブデバイス: 1/1 サブデバイス #0: subdevice #0 |
以下のコマンドで、マイクからの入力がそのまま、イヤホンジャックから出力されます。
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$ arecord -f cd -Dhw:0 | aplay -Dhw:0 録音中 WAVE 'stdin' : Signed 16 bit Little Endian, レート 44100 Hz, ステレオ 再生中 WAVE 'stdin' : Signed 16 bit Little Endian, レート 44100 Hz, ステレオ |
Juliusのインストールと設定
Juliusとは、日本発の音声認識システムを開発・研究するためのオープンソースソフトウェアです。
↓Juliusの公式サイト
最新ソースは、GitHubで公開されています。
↓Julius: Open-Source Large Vocabulary Continuous Speech Recognition Engine
・https://github.com/julius-speech/julius
ライブラリのインストール
まず、ライブラリをインストールします。
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$ sudo apt install -y libasound2-dev libesd0-dev libsndfile1-dev |
Juliusのダウンロードとビルド
Juliusをダウンロードしてビルドします。
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$ git clone https://github.com/julius-speech/julius.git $ cd julius $ ./configure --with-mictype=alsa $ make $ sudo make install |
音声認識パッケージと記述文法音声実行キットのダウンロードと展開
以下のパッケージをダウンロードして展開します。
Julius音声認識パッケージ
Julius を動かしてみるために必要なキットです。 日本語のディクテーション（自動口述筆記）に必要な最小限のモデルおよび Julius の実行バイナリが含まれていますので，これだけでとりあえず Julius を動かしてみることができます。
・https://osdn.net/dl/julius/dictation-kit-4.5.zip
Julius記述文法音声認識実行キット
Julius で記述文法音声認識を動かしてみるためのキットです。日本語の不特定話者音響モデル，およびサンプル文法が含まれています。
・https://github.com/julius-speech/grammar-kit/archive/v4.3.1.zip
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$ cd ~ $ wget https://osdn.net/dl/julius/dictation-kit-4.5.zip $ wget https://github.com/julius-speech/grammar-kit/archive/v4.3.1.zip $ mkdir -p ~/lib/julius $ cd ~/lib/julius $ unzip ~/dictation-kit-4.5.zip $ unzip ~/v4.3.1.zip |
Juliusをコマンドから実行
Juliusをコマンドで実行してみます。
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$ julius -C ~/lib/julius/dictation-kit-4.5/main.jconf -C ~/lib/julius/dictation-kit-4.5/am-gmm.jconf -nostrip -demo -input mic STAT: include config: /home/pi/lib/julius/dictation-kit-4.5/main.jconf STAT: include config: /home/pi/lib/julius/dictation-kit-4.5/am-gmm.jconf STAT: jconf successfully finalized STAT: *** loading AM00 _default Stat: init_phmm: Reading in HMM definition Stat: binhmm-header: variance inversed Stat: read_binhmm: has inversed variances Stat: read_binhmm: binary format HMM definition Stat: read_binhmm: this HMM does not need multipath handling Stat: init_phmm: defined HMMs: 8443 Stat: init_phmm: loading binary hmmlist Stat: load_hmmlist_bin: reading hmmlist Stat: aptree_read: 42857 nodes (21428 branch + 21429 data) Stat: load_hmmlist_bin: reading pseudo phone set Stat: aptree_read: 3253 nodes (1626 branch + 1627 data) Stat: init_phmm: logical names: 21429 in HMMList Stat: init_phmm: base phones: 43 used in logical Stat: init_phmm: finished reading HMM definitions STAT: pseudo phones are loaded from binary hmmlist file Stat: hmm_lookup: 12 pseudo phones are added to logical HMM list STAT: *** AM00 _default loaded STAT: *** loading LM00 _default Stat: init_voca: read 64274 words Stat: init_ngram: reading in binary n-gram from /home/pi/lib/julius/dictation-kit-4.5/model/lang_m/bccwj.60k.bingram Stat: ngram_read_bin: file version: 5 Stat: ngram_read_bin_v5: this is backward 3-gram file stat: ngram_read_bin_v5: reading 1-gram stat: ngram_read_bin_v5: reading 2-gram stat: ngram_read_bin_v5: reading 3-gram Stat: ngram_read_bin_v5: reading additional LR 2-gram Stat: ngram_read_bin: making entry name index Stat: init_ngram: found unknown word entry "<unk>" Stat: init_ngram: finished reading n-gram Stat: init_ngram: mapping dictonary words to n-gram entries Stat: init_ngram: finished word-to-ngram mapping Warning: EOS word "</s>" has unigram prob of "-99" Warning: assigining value of BOS word "<s>": -2.048938 STAT: *** LM00 _default loaded STAT: ------ STAT: All models are ready, go for final fusion STAT: [1] create MFCC extraction instance(s) STAT: *** create MFCC calculation modules from AM STAT: AM 0 _default: create a new module MFCC01 STAT: 1 MFCC modules created STAT: [2] create recognition processing instance(s) with AM and LM STAT: composing recognizer instance SR00 _default (AM00 _default, LM00 _default) STAT: Building HMM lexicon tree STAT: lexicon size: 392049+23665=415714 STAT: coordination check passed STAT: make successor lists for unigram factoring STAT: done STAT: 1-gram factoring values has been pre-computed STAT: SR00 _default composed STAT: [3] initialize for acoustic HMM calculation Stat: outprob_init: state-level mixture PDFs, use calc_mix() Stat: addlog: generating addlog table (size = 1953 kB) Stat: addlog: addlog table generated STAT: [4] prepare MFCC storage(s) STAT: [5] prepare for real-time decoding STAT: All init successfully done STAT: ###### initialize input device ----------------------- System Information begin --------------------- JuliusLib rev.4.5 (fast) Engine specification: - Base setup : fast - Supported LM : DFA, N-gram, Word - Extension : LibSndFile - Compiled by : gcc -g -O2 Library configuration: version 4.5 - Audio input primary A/D-in driver : alsa (Advanced Linux Sound Architecture) available drivers : alsa wavefile formats : various formats by libsndfile ver.1 max. length of an input : 320000 samples, 150 words - Language Model class N-gram support : yes MBR weight support : yes word id unit : short (2 bytes) - Acoustic Model multi-path treatment : autodetect - External library file decompression by : zlib library - Process hangling fork on adinnet input : no - built-in SIMD instruction set for DNN NONE AVAILABLE, DNN computation may be too slow! ------------------------------------------------------------ Configuration of Modules Number of defined modules: AM=1, LM=1, SR=1 Acoustic Model (with input parameter spec.): - AM00 "_default" hmmfilename=/home/pi/lib/julius/dictation-kit-4.5/model/phone_m/jnas-tri-3k16-gid.binhmm hmmmapfilename=/home/pi/lib/julius/dictation-kit-4.5/model/phone_m/logicalTri-3k16-gid.bin Language Model: - LM00 "_default" vocabulary filename=/home/pi/lib/julius/dictation-kit-4.5/model/lang_m/bccwj.60k.htkdic n-gram filename=/home/pi/lib/julius/dictation-kit-4.5/model/lang_m/bccwj.60k.bingram (binary format) Recognizer: - SR00 "_default" (AM00, LM00) ------------------------------------------------------------ Speech Analysis Module(s) [MFCC01] for [AM00 _default] Acoustic analysis condition: parameter = MFCC_E_D_N_Z (25 dim. from 12 cepstrum + energy, abs energy supressed with CMN) sample frequency = 16000 Hz sample period = 625 (1 = 100ns) window size = 400 samples (25.0 ms) frame shift = 160 samples (10.0 ms) pre-emphasis = 0.97 # filterbank = 24 cepst. lifter = 22 raw energy = False energy normalize = False delta window = 2 frames (20.0 ms) around hi freq cut = OFF lo freq cut = OFF zero mean frame = ON use power = OFF CVN = OFF VTLN = OFF spectral subtraction = off cep. mean normalization = yes, real-time MAP-CMN, updating initial mean with last 500 input frames initial mean from file = N/A beginning data weight = 100.00 cep. var. normalization = no base setup from = Julius defaults ------------------------------------------------------------ Acoustic Model(s) [AM00 "_default"] HMM Info: 8443 models, 3090 states, 3090 mpdfs, 49440 Gaussians are defined model type = context dependency handling ON training parameter = MFCC_E_N_D_Z vector length = 25 number of stream = 1 stream info = [0-24] cov. matrix type = DIAGC duration type = NULLD max mixture size = 16 Gaussians max length of model = 5 states logical base phones = 43 model skip trans. = not exist, no multi-path handling AM Parameters: Gaussian pruning = none (full computation) (-gprune) short pause HMM name = "sp" specified, "sp" applied (physical) (-sp) cross-word CD on pass1 = handle by approx. (use 3-best of same LC) ------------------------------------------------------------ Language Model(s) [LM00 "_default"] type=n-gram N-gram info: spec = 3-gram, backward (right-to-left) OOV word = <unk>(id=2) wordset size = 59084 1-gram entries = 59084 ( 0.5 MB) 2-gram entries = 2476660 ( 27.7 MB) (64% are valid contexts) 3-gram entries = 7894442 ( 52.8 MB) LR 2-gram entries= 2476660 ( 9.7 MB) pass1 = given additional forward 2-gram Vocabulary Info: vocabulary size = 64274 words, 366102 models average word len = 5.7 models, 17.1 states maximum state num = 54 nodes per word transparent words = not exist words under class = 9444 words Parameters: (-silhead)head sil word = 0: "<s> @0.000000 [] silB(silB)" (-siltail)tail sil word = 1: "</s> @0.000000 [。] silE(silE)" ------------------------------------------------------------ Recognizer(s) [SR00 "_default"] AM00 "_default" + LM00 "_default" Lexicon tree: total node num = 415714 root node num = 632 (148 hi-freq. words are separated from tree lexicon) leaf node num = 64274 fact. node num = 64274 Inter-word N-gram cache: root node to be cached = 195 / 631 (isolated only) word ends to be cached = 59084 (all) max. allocation size = 46MB (-lmp) pass1 LM weight = 10.0 ins. penalty = +0.0 (-lmp2) pass2 LM weight = 10.0 ins. penalty = +0.0 (-transp)trans. penalty = +0.0 per word (-cmalpha)CM alpha coef = 0.050000 Search parameters: multi-path handling = no (-b) trellis beam width = 1500 (-bs)score pruning thres= disabled (-n)search candidate num= 30 (-s) search stack size = 500 (-m) search overflow = after 10000 hypothesis poped 2nd pass method = searching sentence, generating N-best (-b2) pass2 beam width = 100 (-lookuprange)lookup range= 5 (tm-5 <= t <tm+5) (-sb)2nd scan beamthres = 80.0 (in logscore) (-n) search till = 30 candidates found (-output) and output = 1 candidates out of above IWCD handling: 1st pass: approximation (use 3-best of same LC) 2nd pass: loose (apply when hypo. is popped and scanned) factoring score: 1-gram prob. (statically assigned beforehand) progressive output on 1st pass short pause segmentation = off progout interval = 300 msec fall back on search fail = off, returns search failure ------------------------------------------------------------ Decoding algorithm: 1st pass input processing = real time, on-the-fly 1st pass method = 1-best approx. generating indexed trellis output word confidence measure based on search-time scores ------------------------------------------------------------ FrontEnd: Input stream: input type = waveform input source = microphone device API = default sampling freq. = 16000 Hz threaded A/D-in = supported, on zero frames stripping = off silence cutting = on level thres = 800 / 32767 zerocross thres = 60 / sec. head margin = 300 msec. tail margin = 300 msec. chunk size = 1000 samples FVAD switch value = 3 (0: moderate - 3: very aggressive to regist to noise FVAD param smoothlen = 5 (50ms) FVAD param threshold = 0.50 long-term DC removal = off level scaling factor = 1.00 (disabled) reject short input = < 800 msec reject long input = off ----------------------- System Information end ----------------------- Notice for feature extraction (01), ************************************************************* * Cepstral mean normalization for real-time decoding: * * NOTICE: The first input may not be recognized, since * * no initial mean is available on startup. * ************************************************************* Error: adin_alsa: cannot set PCM channel to 1 (Invalid argument) failed to begin input stream |
エラー？？？
Error: adin_alsa: cannot set PCM channel to 1 (Invalid argument)
failed to begin input stream
マイクを環境変数から設定します。
export ALSADEV="plughw:0,0"
0,0はそれぞれ「カード番号,サブデバイス番号」です。
再度、Juliusを実行します。
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$ julius -C ~/lib/julius/dictation-kit-4.5/main.jconf -C ~/lib/julius/dictation-kit-4.5/am-gmm.jconf -nostrip -demo -input mic STAT: include config: /home/pi/lib/julius/dictation-kit-4.5/main.jconf STAT: include config: /home/pi/lib/julius/dictation-kit-4.5/am-gmm.jconf STAT: jconf successfully finalized STAT: *** loading AM00 _default Stat: init_phmm: Reading in HMM definition Stat: binhmm-header: variance inversed Stat: read_binhmm: has inversed variances Stat: read_binhmm: binary format HMM definition Stat: read_binhmm: this HMM does not need multipath handling Stat: init_phmm: defined HMMs: 8443 Stat: init_phmm: loading binary hmmlist Stat: load_hmmlist_bin: reading hmmlist Stat: aptree_read: 42857 nodes (21428 branch + 21429 data) Stat: load_hmmlist_bin: reading pseudo phone set Stat: aptree_read: 3253 nodes (1626 branch + 1627 data) Stat: init_phmm: logical names: 21429 in HMMList Stat: init_phmm: base phones: 43 used in logical Stat: init_phmm: finished reading HMM definitions STAT: pseudo phones are loaded from binary hmmlist file Stat: hmm_lookup: 12 pseudo phones are added to logical HMM list STAT: *** AM00 _default loaded STAT: *** loading LM00 _default Stat: init_voca: read 64274 words Stat: init_ngram: reading in binary n-gram from /home/pi/lib/julius/dictation-kit-4.5/model/lang_m/bccwj.60k.bingram Stat: ngram_read_bin: file version: 5 Stat: ngram_read_bin_v5: this is backward 3-gram file stat: ngram_read_bin_v5: reading 1-gram stat: ngram_read_bin_v5: reading 2-gram stat: ngram_read_bin_v5: reading 3-gram Stat: ngram_read_bin_v5: reading additional LR 2-gram Stat: ngram_read_bin: making entry name index Stat: init_ngram: found unknown word entry "<unk>" Stat: init_ngram: finished reading n-gram Stat: init_ngram: mapping dictonary words to n-gram entries Stat: init_ngram: finished word-to-ngram mapping Warning: EOS word "</s>" has unigram prob of "-99" Warning: assigining value of BOS word "<s>": -2.048938 STAT: *** LM00 _default loaded STAT: ------ STAT: All models are ready, go for final fusion STAT: [1] create MFCC extraction instance(s) STAT: *** create MFCC calculation modules from AM STAT: AM 0 _default: create a new module MFCC01 STAT: 1 MFCC modules created STAT: [2] create recognition processing instance(s) with AM and LM STAT: composing recognizer instance SR00 _default (AM00 _default, LM00 _default) STAT: Building HMM lexicon tree STAT: lexicon size: 392049+23665=415714 STAT: coordination check passed STAT: make successor lists for unigram factoring STAT: done STAT: 1-gram factoring values has been pre-computed STAT: SR00 _default composed STAT: [3] initialize for acoustic HMM calculation Stat: outprob_init: state-level mixture PDFs, use calc_mix() Stat: addlog: generating addlog table (size = 1953 kB) Stat: addlog: addlog table generated STAT: [4] prepare MFCC storage(s) STAT: [5] prepare for real-time decoding STAT: All init successfully done STAT: ###### initialize input device ----------------------- System Information begin --------------------- JuliusLib rev.4.5 (fast) Engine specification: - Base setup : fast - Supported LM : DFA, N-gram, Word - Extension : LibSndFile - Compiled by : gcc -g -O2 Library configuration: version 4.5 - Audio input primary A/D-in driver : alsa (Advanced Linux Sound Architecture) available drivers : alsa wavefile formats : various formats by libsndfile ver.1 max. length of an input : 320000 samples, 150 words - Language Model class N-gram support : yes MBR weight support : yes word id unit : short (2 bytes) - Acoustic Model multi-path treatment : autodetect - External library file decompression by : zlib library - Process hangling fork on adinnet input : no - built-in SIMD instruction set for DNN NONE AVAILABLE, DNN computation may be too slow! ------------------------------------------------------------ Configuration of Modules Number of defined modules: AM=1, LM=1, SR=1 Acoustic Model (with input parameter spec.): - AM00 "_default" hmmfilename=/home/pi/lib/julius/dictation-kit-4.5/model/phone_m/jnas-tri-3k16-gid.binhmm hmmmapfilename=/home/pi/lib/julius/dictation-kit-4.5/model/phone_m/logicalTri-3k16-gid.bin Language Model: - LM00 "_default" vocabulary filename=/home/pi/lib/julius/dictation-kit-4.5/model/lang_m/bccwj.60k.htkdic n-gram filename=/home/pi/lib/julius/dictation-kit-4.5/model/lang_m/bccwj.60k.bingram (binary format) Recognizer: - SR00 "_default" (AM00, LM00) ------------------------------------------------------------ Speech Analysis Module(s) [MFCC01] for [AM00 _default] Acoustic analysis condition: parameter = MFCC_E_D_N_Z (25 dim. from 12 cepstrum + energy, abs energy supressed with CMN) sample frequency = 16000 Hz sample period = 625 (1 = 100ns) window size = 400 samples (25.0 ms) frame shift = 160 samples (10.0 ms) pre-emphasis = 0.97 # filterbank = 24 cepst. lifter = 22 raw energy = False energy normalize = False delta window = 2 frames (20.0 ms) around hi freq cut = OFF lo freq cut = OFF zero mean frame = ON use power = OFF CVN = OFF VTLN = OFF spectral subtraction = off cep. mean normalization = yes, real-time MAP-CMN, updating initial mean with last 500 input frames initial mean from file = N/A beginning data weight = 100.00 cep. var. normalization = no base setup from = Julius defaults ------------------------------------------------------------ Acoustic Model(s) [AM00 "_default"] HMM Info: 8443 models, 3090 states, 3090 mpdfs, 49440 Gaussians are defined model type = context dependency handling ON training parameter = MFCC_E_N_D_Z vector length = 25 number of stream = 1 stream info = [0-24] cov. matrix type = DIAGC duration type = NULLD max mixture size = 16 Gaussians max length of model = 5 states logical base phones = 43 model skip trans. = not exist, no multi-path handling AM Parameters: Gaussian pruning = none (full computation) (-gprune) short pause HMM name = "sp" specified, "sp" applied (physical) (-sp) cross-word CD on pass1 = handle by approx. (use 3-best of same LC) ------------------------------------------------------------ Language Model(s) [LM00 "_default"] type=n-gram N-gram info: spec = 3-gram, backward (right-to-left) OOV word = <unk>(id=2) wordset size = 59084 1-gram entries = 59084 ( 0.5 MB) 2-gram entries = 2476660 ( 27.7 MB) (64% are valid contexts) 3-gram entries = 7894442 ( 52.8 MB) LR 2-gram entries= 2476660 ( 9.7 MB) pass1 = given additional forward 2-gram Vocabulary Info: vocabulary size = 64274 words, 366102 models average word len = 5.7 models, 17.1 states maximum state num = 54 nodes per word transparent words = not exist words under class = 9444 words Parameters: (-silhead)head sil word = 0: "<s> @0.000000 [] silB(silB)" (-siltail)tail sil word = 1: "</s> @0.000000 [。] silE(silE)" ------------------------------------------------------------ Recognizer(s) [SR00 "_default"] AM00 "_default" + LM00 "_default" Lexicon tree: total node num = 415714 root node num = 632 (148 hi-freq. words are separated from tree lexicon) leaf node num = 64274 fact. node num = 64274 Inter-word N-gram cache: root node to be cached = 195 / 631 (isolated only) word ends to be cached = 59084 (all) max. allocation size = 46MB (-lmp) pass1 LM weight = 10.0 ins. penalty = +0.0 (-lmp2) pass2 LM weight = 10.0 ins. penalty = +0.0 (-transp)trans. penalty = +0.0 per word (-cmalpha)CM alpha coef = 0.050000 Search parameters: multi-path handling = no (-b) trellis beam width = 1500 (-bs)score pruning thres= disabled (-n)search candidate num= 30 (-s) search stack size = 500 (-m) search overflow = after 10000 hypothesis poped 2nd pass method = searching sentence, generating N-best (-b2) pass2 beam width = 100 (-lookuprange)lookup range= 5 (tm-5 <= t <tm+5) (-sb)2nd scan beamthres = 80.0 (in logscore) (-n) search till = 30 candidates found (-output) and output = 1 candidates out of above IWCD handling: 1st pass: approximation (use 3-best of same LC) 2nd pass: loose (apply when hypo. is popped and scanned) factoring score: 1-gram prob. (statically assigned beforehand) progressive output on 1st pass short pause segmentation = off progout interval = 300 msec fall back on search fail = off, returns search failure ------------------------------------------------------------ Decoding algorithm: 1st pass input processing = real time, on-the-fly 1st pass method = 1-best approx. generating indexed trellis output word confidence measure based on search-time scores ------------------------------------------------------------ FrontEnd: Input stream: input type = waveform input source = microphone device API = default sampling freq. = 16000 Hz threaded A/D-in = supported, on zero frames stripping = off silence cutting = on level thres = 800 / 32767 zerocross thres = 60 / sec. head margin = 300 msec. tail margin = 300 msec. chunk size = 1000 samples FVAD switch value = 3 (0: moderate - 3: very aggressive to regist to noise FVAD param smoothlen = 5 (50ms) FVAD param threshold = 0.50 long-term DC removal = off level scaling factor = 1.00 (disabled) reject short input = < 800 msec reject long input = off ----------------------- System Information end ----------------------- Notice for feature extraction (01), ************************************************************* * Cepstral mean normalization for real-time decoding: * * NOTICE: The first input may not be recognized, since * * no initial mean is available on startup. * ************************************************************* Stat: adin_alsa: device name from ALSADEV: "plughw:0,0" Stat: capture audio at 16000Hz Stat: adin_alsa: latency set to 32 msec (chunk = 512 bytes) Stat: "plughw:0,0": seeed2micvoicec [seeed-2mic-voicecard] device bcm2835-i2s-wm8960-hifi wm8960-hifi-0 [] subdevice #0 STAT: AD-in thread created <<< please speak >>> |
<<< please speak >>>
となったらOKです。
これで、マイクからの待ち状態なので、マイクに向かってしゃべってみます。
誤認識が多い。。
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