From 7b03f2718318d24cf23829412b9638377b0a49d6 Mon Sep 17 00:00:00 2001 From: csukuangfj Date: Wed, 12 Jun 2024 16:02:46 +0000 Subject: [PATCH] deploy: 13f55d073513b3beaefdf0b7e16237b35199ca04 --- _sources/docker/intro.rst.txt | 2 + .../fst-based-forced-alignment/diff.rst.txt | 41 + .../fst-based-forced-alignment/index.rst.txt | 18 + .../k2-based.rst.txt | 4 + .../kaldi-based.rst.txt | 712 +++++++++++++++ _sources/index.rst.txt | 4 +- .../export-ncnn-conv-emformer.rst.txt | 4 +- .../model-export/export-ncnn-lstm.rst.txt | 4 +- .../export-ncnn-zipformer.rst.txt | 4 +- ...-SRI-VOiCES-src-sp0307-ch127535-sg0042.wav | Bin 0 -> 108844 bytes _static/kaldi-align/at.wav | Bin 0 -> 2620 bytes _static/kaldi-align/beside.wav | Bin 0 -> 14206 bytes _static/kaldi-align/curiosity.wav | Bin 0 -> 22576 bytes _static/kaldi-align/had.wav | Bin 0 -> 4550 bytes _static/kaldi-align/i.wav | Bin 0 -> 688 bytes _static/kaldi-align/me.wav | Bin 0 -> 2620 bytes _static/kaldi-align/moment.wav | Bin 0 -> 9702 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_sources/fst-based-forced-alignment/diff.rst.txt create mode 100644 _sources/fst-based-forced-alignment/index.rst.txt create mode 100644 _sources/fst-based-forced-alignment/k2-based.rst.txt create mode 100644 _sources/fst-based-forced-alignment/kaldi-based.rst.txt create mode 100644 _static/kaldi-align/Lab41-SRI-VOiCES-src-sp0307-ch127535-sg0042.wav create mode 100644 _static/kaldi-align/at.wav create mode 100644 _static/kaldi-align/beside.wav create mode 100644 _static/kaldi-align/curiosity.wav create mode 100644 _static/kaldi-align/had.wav create mode 100644 _static/kaldi-align/i.wav create mode 100644 _static/kaldi-align/me.wav create mode 100644 _static/kaldi-align/moment.wav create mode 100644 _static/kaldi-align/that.wav create mode 100644 _static/kaldi-align/this.wav create mode 100644 fst-based-forced-alignment/diff.html create mode 100644 fst-based-forced-alignment/index.html create mode 100644 fst-based-forced-alignment/k2-based.html create mode 100644 fst-based-forced-alignment/kaldi-based.html diff --git a/_sources/docker/intro.rst.txt b/_sources/docker/intro.rst.txt index 2f4bdb3f6..f3d2b0727 100644 --- a/_sources/docker/intro.rst.txt +++ b/_sources/docker/intro.rst.txt @@ -34,6 +34,8 @@ which will give you something like below: .. code-block:: bash + "torch2.3.1-cuda12.1" + "torch2.3.1-cuda11.8" "torch2.2.2-cuda12.1" "torch2.2.2-cuda11.8" "torch2.2.1-cuda12.1" diff --git a/_sources/fst-based-forced-alignment/diff.rst.txt b/_sources/fst-based-forced-alignment/diff.rst.txt new file mode 100644 index 000000000..56b6c430e --- /dev/null +++ b/_sources/fst-based-forced-alignment/diff.rst.txt @@ -0,0 +1,41 @@ +Two approaches +============== + +Two approaches for FST-based forced alignment will be described: + + - `Kaldi`_-based + - `k2`_-based + +Note that the `Kaldi`_-based approach does not depend on `Kaldi`_ at all. +That is, you don't need to install `Kaldi`_ in order to use it. Instead, +we use `kaldi-decoder`_, which has ported the C++ decoding code from `Kaldi`_ +without depending on it. + +Differences between the two approaches +-------------------------------------- + +The following table compares the differences between the two approaches. + +.. list-table:: + + * - Features + - `Kaldi`_-based + - `k2`_-based + * - Support CUDA + - No + - Yes + * - Support CPU + - Yes + - Yes + * - Support batch processing + - No + - Yes on CUDA; No on CPU + * - Support streaming models + - Yes + - No + * - Support C++ APIs + - Yes + - Yes + * - Support Python APIs + - Yes + - Yes diff --git a/_sources/fst-based-forced-alignment/index.rst.txt b/_sources/fst-based-forced-alignment/index.rst.txt new file mode 100644 index 000000000..92a05faaa --- /dev/null +++ b/_sources/fst-based-forced-alignment/index.rst.txt @@ -0,0 +1,18 @@ +FST-based forced alignment +========================== + +This section describes how to perform **FST-based** ``forced alignment`` with models +trained by `CTC`_ loss. + +We use `CTC FORCED ALIGNMENT API TUTORIAL `_ +from `torchaudio`_ as a reference in this section. + +Different from `torchaudio`_, we use an ``FST``-based approach. + +.. toctree:: + :maxdepth: 2 + :caption: Contents: + + diff + kaldi-based + k2-based diff --git a/_sources/fst-based-forced-alignment/k2-based.rst.txt b/_sources/fst-based-forced-alignment/k2-based.rst.txt new file mode 100644 index 000000000..373e49f3e --- /dev/null +++ b/_sources/fst-based-forced-alignment/k2-based.rst.txt @@ -0,0 +1,4 @@ +k2-based forced alignment +========================= + +TODO(fangjun) diff --git a/_sources/fst-based-forced-alignment/kaldi-based.rst.txt b/_sources/fst-based-forced-alignment/kaldi-based.rst.txt new file mode 100644 index 000000000..69b6a665b --- /dev/null +++ b/_sources/fst-based-forced-alignment/kaldi-based.rst.txt @@ -0,0 +1,712 @@ +Kaldi-based forced alignment +============================ + +This section describes in detail how to use `kaldi-decoder`_ +for **FST-based** ``forced alignment`` with models trained by `CTC`_ loss. + +.. hint:: + + We have a colab notebook walking you through this section step by step. + + |kaldi-based forced alignment colab notebook| + + .. |kaldi-based forced alignment colab notebook| image:: https://colab.research.google.com/assets/colab-badge.svg + :target: https://github.com/k2-fsa/colab/blob/master/icefall/ctc_forced_alignment_fst_based_kaldi.ipynb + +Prepare the environment +----------------------- + +Before you continue, make sure you have setup `icefall`_ by following :ref:`install icefall`. + +.. hint:: + + You don't need to install `Kaldi`_. We will ``NOT`` use `Kaldi`_ below. + +Get the test data +----------------- + +We use the test wave +from `CTC FORCED ALIGNMENT API TUTORIAL `_ + +.. code-block:: python3 + + import torchaudio + + # Download test wave + speech_file = torchaudio.utils.download_asset("tutorial-assets/Lab41-SRI-VOiCES-src-sp0307-ch127535-sg0042.wav") + print(speech_file) + waveform, sr = torchaudio.load(speech_file) + transcript = "i had that curiosity beside me at this moment".split() + print(waveform.shape, sr) + + assert waveform.ndim == 2 + assert waveform.shape[0] == 1 + assert sr == 16000 + +The test wave is downloaded to:: + + $HOME/.cache/torch/hub/torchaudio/tutorial-assets/Lab41-SRI-VOiCES-src-sp0307-ch127535-sg0042.wav + +.. raw:: html + + + + + + + + + + + + +
Wave filenameContentText
Lab41-SRI-VOiCES-src-sp0307-ch127535-sg0042.wav + + + i had that curiosity beside me at this moment +
+ +We use the test model +from `CTC FORCED ALIGNMENT API TUTORIAL `_ + +.. code-block:: python3 + + import torch + + bundle = torchaudio.pipelines.MMS_FA + + device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + model = bundle.get_model(with_star=False).to(device) + +The model is downloaded to:: + + $HOME/.cache/torch/hub/checkpoints/model.pt + +Compute log_probs +----------------- + +.. code-block:: bash + + with torch.inference_mode(): + emission, _ = model(waveform.to(device)) + print(emission.shape) + +It should print:: + + torch.Size([1, 169, 28]) + +Create token2id and id2token +---------------------------- + +.. code-block:: python3 + + token2id = bundle.get_dict(star=None) + id2token = {i:t for t, i in token2id.items()} + token2id[""] = 0 + del token2id["-"] + +Create word2id and id2word +-------------------------- + +.. code-block:: python3 + + words = list(set(transcript)) + word2id = dict() + word2id['eps'] = 0 + for i, w in enumerate(words): + word2id[w] = i + 1 + + id2word = {i:w for w, i in word2id.items()} + +Note that we only use words from the transcript of the test wave. + +Generate lexicon-related files +------------------------------ + +We use the code below to generate the following 4 files: + + - ``lexicon.txt`` + - ``tokens.txt`` + - ``words.txt`` + - ``lexicon_disambig.txt`` + +.. caution:: + + ``words.txt`` contains only words from the transcript of the test wave. + +.. code-block:: python3 + + from prepare_lang import add_disambig_symbols + + lexicon = [(w, list(w)) for w in word2id if w != "eps"] + lexicon_disambig, max_disambig_id = add_disambig_symbols(lexicon) + + with open('lexicon.txt', 'w', encoding='utf-8') as f: + for w, tokens in lexicon: + f.write(f"{w} {' '.join(tokens)}\n") + + with open('lexicon_disambig.txt', 'w', encoding='utf-8') as f: + for w, tokens in lexicon_disambig: + f.write(f"{w} {' '.join(tokens)}\n") + + with open('tokens.txt', 'w', encoding='utf-8') as f: + for t, i in token2id.items(): + if t == '-': + t = "" + f.write(f"{t} {i}\n") + + for k in range(max_disambig_id + 2): + f.write(f"#{k} {len(token2id) + k}\n") + + with open('words.txt', 'w', encoding='utf-8') as f: + for w, i in word2id.items(): + f.write(f"{w} {i}\n") + f.write(f'#0 {len(word2id)}\n') + + +To give you an idea about what the generated files look like:: + + head -n 50 lexicon.txt lexicon_disambig.txt tokens.txt words.txt + +prints:: + + ==> lexicon.txt <== + moment m o m e n t + beside b e s i d e + i i + this t h i s + curiosity c u r i o s i t y + had h a d + that t h a t + at a t + me m e + + ==> lexicon_disambig.txt <== + moment m o m e n t + beside b e s i d e + i i + this t h i s + curiosity c u r i o s i t y + had h a d + that t h a t + at a t + me m e + + ==> tokens.txt <== + a 1 + i 2 + e 3 + n 4 + o 5 + u 6 + t 7 + s 8 + r 9 + m 10 + k 11 + l 12 + d 13 + g 14 + h 15 + y 16 + b 17 + p 18 + w 19 + c 20 + v 21 + j 22 + z 23 + f 24 + ' 25 + q 26 + x 27 + 0 + #0 28 + #1 29 + + ==> words.txt <== + eps 0 + moment 1 + beside 2 + i 3 + this 4 + curiosity 5 + had 6 + that 7 + at 8 + me 9 + #0 10 + +.. note:: + + This test model uses characters as modeling unit. If you use other types of + modeling unit, the same code can be used without any change. + +Convert transcript to an FST graph +---------------------------------- + +.. code-block:: bash + + egs/librispeech/ASR/local/prepare_lang_fst.py --lang-dir ./ + +The above command should generate two files ``H.fst`` and ``HL.fst``. We will +use ``HL.fst`` below:: + + -rw-r--r-- 1 root root 13K Jun 12 08:28 H.fst + -rw-r--r-- 1 root root 3.7K Jun 12 08:28 HL.fst + +Force aligner +------------- + +Now, everything is ready. We can use the following code to get forced alignments. + +.. code-block:: python3 + + from kaldi_decoder import DecodableCtc, FasterDecoder, FasterDecoderOptions + import kaldifst + + def force_align(): + HL = kaldifst.StdVectorFst.read("./HL.fst") + decodable = DecodableCtc(emission[0].contiguous().cpu().numpy()) + decoder_opts = FasterDecoderOptions(max_active=3000) + decoder = FasterDecoder(HL, decoder_opts) + decoder.decode(decodable) + if not decoder.reached_final(): + print(f"failed to decode xxx") + return None + ok, best_path = decoder.get_best_path() + + ( + ok, + isymbols_out, + osymbols_out, + total_weight, + ) = kaldifst.get_linear_symbol_sequence(best_path) + if not ok: + print(f"failed to get linear symbol sequence for xxx") + return None + + # We need to use i-1 here since we have incremented tokens during + # HL construction + alignment = [i-1 for i in isymbols_out] + return alignment + + alignment = force_align() + + for i, a in enumerate(alignment): + print(i, id2token[a]) + +The output should be identical to +``_. + +For ease of reference, we list the output below:: + + 0 - + 1 - + 2 - + 3 - + 4 - + 5 - + 6 - + 7 - + 8 - + 9 - + 10 - + 11 - + 12 - + 13 - + 14 - + 15 - + 16 - + 17 - + 18 - + 19 - + 20 - + 21 - + 22 - + 23 - + 24 - + 25 - + 26 - + 27 - + 28 - + 29 - + 30 - + 31 - + 32 i + 33 - + 34 - + 35 h + 36 h + 37 a + 38 - + 39 - + 40 - + 41 d + 42 - + 43 - + 44 t + 45 h + 46 - + 47 a + 48 - + 49 - + 50 t + 51 - + 52 - + 53 - + 54 c + 55 - + 56 - + 57 - + 58 u + 59 u + 60 - + 61 - + 62 - + 63 r + 64 - + 65 i + 66 - + 67 - + 68 - + 69 - + 70 - + 71 - + 72 o + 73 - + 74 - + 75 - + 76 - + 77 - + 78 - + 79 s + 80 - + 81 - + 82 - + 83 i + 84 - + 85 t + 86 - + 87 - + 88 y + 89 - + 90 - + 91 - + 92 - + 93 b + 94 - + 95 e + 96 - + 97 - + 98 - + 99 - + 100 - + 101 s + 102 - + 103 - + 104 - + 105 - + 106 - + 107 - + 108 - + 109 - + 110 i + 111 - + 112 - + 113 d + 114 e + 115 - + 116 m + 117 - + 118 - + 119 e + 120 - + 121 - + 122 - + 123 - + 124 a + 125 - + 126 - + 127 t + 128 - + 129 t + 130 h + 131 - + 132 i + 133 - + 134 - + 135 - + 136 s + 137 - + 138 - + 139 - + 140 - + 141 m + 142 - + 143 - + 144 o + 145 - + 146 - + 147 - + 148 m + 149 - + 150 - + 151 e + 152 - + 153 n + 154 - + 155 t + 156 - + 157 - + 158 - + 159 - + 160 - + 161 - + 162 - + 163 - + 164 - + 165 - + 166 - + 167 - + 168 - + +To merge tokens, we use:: + + from icefall.ctc import merge_tokens + token_spans = merge_tokens(alignment) + for span in token_spans: + print(id2token[span.token], span.start, span.end) + +The output is given below:: + + i 32 33 + h 35 37 + a 37 38 + d 41 42 + t 44 45 + h 45 46 + a 47 48 + t 50 51 + c 54 55 + u 58 60 + r 63 64 + i 65 66 + o 72 73 + s 79 80 + i 83 84 + t 85 86 + y 88 89 + b 93 94 + e 95 96 + s 101 102 + i 110 111 + d 113 114 + e 114 115 + m 116 117 + e 119 120 + a 124 125 + t 127 128 + t 129 130 + h 130 131 + i 132 133 + s 136 137 + m 141 142 + o 144 145 + m 148 149 + e 151 152 + n 153 154 + t 155 156 + +All of the code below is copied and modified +from ``_. + +Segment each word using the computed alignments +----------------------------------------------- + +.. code-block:: python3 + + def unflatten(list_, lengths): + assert len(list_) == sum(lengths) + i = 0 + ret = [] + for l in lengths: + ret.append(list_[i : i + l]) + i += l + return ret + + + word_spans = unflatten(token_spans, [len(word) for word in transcript]) + print(word_spans) + +The output is:: + + [[TokenSpan(token=2, start=32, end=33)], + [TokenSpan(token=15, start=35, end=37), TokenSpan(token=1, start=37, end=38), TokenSpan(token=13, start=41, end=42)], + [TokenSpan(token=7, start=44, end=45), TokenSpan(token=15, start=45, end=46), TokenSpan(token=1, start=47, end=48), TokenSpan(token=7, start=50, end=51)], + [TokenSpan(token=20, start=54, end=55), TokenSpan(token=6, start=58, end=60), TokenSpan(token=9, start=63, end=64), TokenSpan(token=2, start=65, end=66), TokenSpan(token=5, start=72, end=73), TokenSpan(token=8, start=79, end=80), TokenSpan(token=2, start=83, end=84), TokenSpan(token=7, start=85, end=86), TokenSpan(token=16, start=88, end=89)], + [TokenSpan(token=17, start=93, end=94), TokenSpan(token=3, start=95, end=96), TokenSpan(token=8, start=101, end=102), TokenSpan(token=2, start=110, end=111), TokenSpan(token=13, start=113, end=114), TokenSpan(token=3, start=114, end=115)], + [TokenSpan(token=10, start=116, end=117), TokenSpan(token=3, start=119, end=120)], + [TokenSpan(token=1, start=124, end=125), TokenSpan(token=7, start=127, end=128)], + [TokenSpan(token=7, start=129, end=130), TokenSpan(token=15, start=130, end=131), TokenSpan(token=2, start=132, end=133), TokenSpan(token=8, start=136, end=137)], + [TokenSpan(token=10, start=141, end=142), TokenSpan(token=5, start=144, end=145), TokenSpan(token=10, start=148, end=149), TokenSpan(token=3, start=151, end=152), TokenSpan(token=4, start=153, end=154), TokenSpan(token=7, start=155, end=156)] + ] + + +.. code-block:: python3 + + def preview_word(waveform, spans, num_frames, transcript, sample_rate=bundle.sample_rate): + ratio = waveform.size(1) / num_frames + x0 = int(ratio * spans[0].start) + x1 = int(ratio * spans[-1].end) + print(f"{transcript} {x0 / sample_rate:.3f} - {x1 / sample_rate:.3f} sec") + segment = waveform[:, x0:x1] + return IPython.display.Audio(segment.numpy(), rate=sample_rate) + num_frames = emission.size(1) + +.. code-block:: python3 + + preview_word(waveform, word_spans[0], num_frames, transcript[0]) + preview_word(waveform, word_spans[1], num_frames, transcript[1]) + preview_word(waveform, word_spans[2], num_frames, transcript[2]) + preview_word(waveform, word_spans[3], num_frames, transcript[3]) + preview_word(waveform, word_spans[4], num_frames, transcript[4]) + preview_word(waveform, word_spans[5], num_frames, transcript[5]) + preview_word(waveform, word_spans[6], num_frames, transcript[6]) + preview_word(waveform, word_spans[7], num_frames, transcript[7]) + preview_word(waveform, word_spans[8], num_frames, transcript[8]) + +The segmented wave of each word along with its time stamp is given below: + +.. raw:: html + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
WordTimeWave
i0.644 - 0.664 sec + +
had0.704 - 0.845 sec + +
that0.885 - 1.026 sec + +
curiosity1.086 - 1.790 sec + +
beside1.871 - 2.314 sec + +
me2.334 - 2.414 sec + +
at2.495 - 2.575 sec + +
this2.595 - 2.756 sec + +
moment2.837 - 3.138 sec + +
+ +We repost the whole wave below for ease of reference: + +.. raw:: html + + + + + + + + + + + + +
Wave filenameContentText
Lab41-SRI-VOiCES-src-sp0307-ch127535-sg0042.wav + + + i had that curiosity beside me at this moment +
+ +Summary +------- + +Congratulations! You have succeeded in using the FST-based approach to +compute alignment of a test wave. diff --git a/_sources/index.rst.txt b/_sources/index.rst.txt index fb539d3f2..d46a4038f 100644 --- a/_sources/index.rst.txt +++ b/_sources/index.rst.txt @@ -25,7 +25,7 @@ speech recognition recipes using `k2 `_. docker/index faqs model-export/index - + fst-based-forced-alignment/index .. toctree:: :maxdepth: 3 @@ -40,5 +40,5 @@ speech recognition recipes using `k2 `_. .. toctree:: :maxdepth: 2 - + decoding-with-langugage-models/index diff --git a/_sources/model-export/export-ncnn-conv-emformer.rst.txt b/_sources/model-export/export-ncnn-conv-emformer.rst.txt index 93392aee7..4cdc25ee6 100644 --- a/_sources/model-export/export-ncnn-conv-emformer.rst.txt +++ b/_sources/model-export/export-ncnn-conv-emformer.rst.txt @@ -15,8 +15,8 @@ We will show you step by step how to export it to `ncnn`_ and run it with `sherp .. caution:: - Please use a more recent version of PyTorch. For instance, ``torch 1.8`` - may ``not`` work. + ``torch > 2.0`` may not work. If you get errors while building pnnx, please switch + to ``torch < 2.0``. 1. Download the pre-trained model --------------------------------- diff --git a/_sources/model-export/export-ncnn-lstm.rst.txt b/_sources/model-export/export-ncnn-lstm.rst.txt index 310c3d8e4..ccf522dec 100644 --- a/_sources/model-export/export-ncnn-lstm.rst.txt +++ b/_sources/model-export/export-ncnn-lstm.rst.txt @@ -15,8 +15,8 @@ We will show you step by step how to export it to `ncnn`_ and run it with `sherp .. caution:: - Please use a more recent version of PyTorch. For instance, ``torch 1.8`` - may ``not`` work. + ``torch > 2.0`` may not work. If you get errors while building pnnx, please switch + to ``torch < 2.0``. 1. Download the pre-trained model ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ diff --git a/_sources/model-export/export-ncnn-zipformer.rst.txt b/_sources/model-export/export-ncnn-zipformer.rst.txt index a5845b0e4..51fc6c8e5 100644 --- a/_sources/model-export/export-ncnn-zipformer.rst.txt +++ b/_sources/model-export/export-ncnn-zipformer.rst.txt @@ -15,8 +15,8 @@ We will show you step by step how to export it to `ncnn`_ and run it with `sherp .. caution:: - Please use a more recent version of PyTorch. For instance, ``torch 1.8`` - may ``not`` work. + ``torch > 2.0`` may not work. If you get errors while building pnnx, please switch + to ``torch < 2.0``. 1. 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  • Frequently Asked Questions (FAQs)
  • Model export
    • +
  • FST-based forced alignment
    • Recipes
      • @@ -136,7 +137,9 @@ docker images:

      which will give you something like below:

      -
      "torch2.2.2-cuda12.1"
+
      "torch2.3.1-cuda12.1"
+"torch2.3.1-cuda11.8"
+"torch2.2.2-cuda12.1"
 "torch2.2.2-cuda11.8"
 "torch2.2.1-cuda12.1"
 "torch2.2.1-cuda11.8"
diff --git a/faqs.html b/faqs.html
index e5315dce8..43ae9d548 100644
--- a/faqs.html
+++ b/faqs.html
@@ -56,6 +56,7 @@
  • Model export
    • +
  • FST-based forced alignment
    • Recipes
      • diff --git a/fst-based-forced-alignment/diff.html b/fst-based-forced-alignment/diff.html new file mode 100644 index 000000000..3d5764290 --- /dev/null +++ b/fst-based-forced-alignment/diff.html @@ -0,0 +1,182 @@ + + + + + + + Two approaches — icefall 0.1 documentation + + + + + + + + + + + + + + + + + + + +
      + + +
      + +
      +
      +
      + +
      +
      +
      +
      + +
      +

      Two approaches

      +

      Two approaches for FST-based forced alignment will be described:

      +
      +
      +
      +

      Note that the Kaldi-based approach does not depend on Kaldi at all. +That is, you don’t need to install Kaldi in order to use it. Instead, +we use kaldi-decoder, which has ported the C++ decoding code from Kaldi +without depending on it.

      +
      +

      Differences between the two approaches

      +

      The following table compares the differences between the two approaches.

      + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

      Features

      Kaldi-based

      k2-based

      Support CUDA

      No

      Yes

      Support CPU

      Yes

      Yes

      Support batch processing

      No

      Yes on CUDA; No on CPU

      Support streaming models

      Yes

      No

      Support C++ APIs

      Yes

      Yes

      Support Python APIs

      Yes

      Yes

      +
      +
      + + +
      +
      + +
      +
      +
      +
      + + + + \ No newline at end of file diff --git a/fst-based-forced-alignment/index.html b/fst-based-forced-alignment/index.html new file mode 100644 index 000000000..3b27b2e82 --- /dev/null +++ b/fst-based-forced-alignment/index.html @@ -0,0 +1,159 @@ + + + + + + + FST-based forced alignment — icefall 0.1 documentation + + + + + + + + + + + + + + + + + + + +
      + + +
      + +
      +
      +
      + +
      +
      +
      +
      + +
      +

      FST-based forced alignment

      +

      This section describes how to perform FST-based forced alignment with models +trained by CTC loss.

      +

      We use CTC FORCED ALIGNMENT API TUTORIAL +from torchaudio as a reference in this section.

      +

      Different from torchaudio, we use an FST-based approach.

      +
      +

      Contents:

      + +
      +
      + + +
      +
      + +
      +
      +
      +
      + + + + \ No newline at end of file diff --git a/fst-based-forced-alignment/k2-based.html b/fst-based-forced-alignment/k2-based.html new file mode 100644 index 000000000..ad25d7eb9 --- /dev/null +++ b/fst-based-forced-alignment/k2-based.html @@ -0,0 +1,133 @@ + + + + + + + k2-based forced alignment — icefall 0.1 documentation + + + + + + + + + + + + + + + + + + + +
      + + +
      + +
      +
      +
      + +
      +
      +
      +
      + +
      +

      k2-based forced alignment

      +

      TODO(fangjun)

      +
      + + +
      +
      + +
      +
      +
      +
      + + + + \ No newline at end of file diff --git a/fst-based-forced-alignment/kaldi-based.html b/fst-based-forced-alignment/kaldi-based.html new file mode 100644 index 000000000..a1ec582e8 --- /dev/null +++ b/fst-based-forced-alignment/kaldi-based.html @@ -0,0 +1,816 @@ + + + + + + + Kaldi-based forced alignment — icefall 0.1 documentation + + + + + + + + + + + + + + + + + + + +
      + + +
      + +
      +
      +
      + +
      +
      +
      +
      + +
      +

      Kaldi-based forced alignment

      +

      This section describes in detail how to use kaldi-decoder +for FST-based forced alignment with models trained by CTC loss.

      +
      +

      Hint

      +

      We have a colab notebook walking you through this section step by step.

      +

      kaldi-based forced alignment colab notebook

      +
      +
      +

      Prepare the environment

      +

      Before you continue, make sure you have setup icefall by following Installation.

      +
      +

      Hint

      +

      You don’t need to install Kaldi. We will NOT use Kaldi below.

      +
      +
      +
      +

      Get the test data

      +

      We use the test wave +from CTC FORCED ALIGNMENT API TUTORIAL

      +
      import torchaudio
+
+# Download test wave
+speech_file = torchaudio.utils.download_asset("tutorial-assets/Lab41-SRI-VOiCES-src-sp0307-ch127535-sg0042.wav")
+print(speech_file)
+waveform, sr = torchaudio.load(speech_file)
+transcript = "i had that curiosity beside me at this moment".split()
+print(waveform.shape, sr)
+
+assert waveform.ndim == 2
+assert waveform.shape[0] == 1
+assert sr == 16000
+
      +
      +

      The test wave is downloaded to:

      +
      $HOME/.cache/torch/hub/torchaudio/tutorial-assets/Lab41-SRI-VOiCES-src-sp0307-ch127535-sg0042.wav
+
      +
      + + + + + + + + + + + +
      Wave filenameContentText
      Lab41-SRI-VOiCES-src-sp0307-ch127535-sg0042.wav + + + i had that curiosity beside me at this moment +

      We use the test model +from CTC FORCED ALIGNMENT API TUTORIAL

      +
      import torch
+
+bundle = torchaudio.pipelines.MMS_FA
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+model = bundle.get_model(with_star=False).to(device)
+
      +
      +

      The model is downloaded to:

      +
      $HOME/.cache/torch/hub/checkpoints/model.pt
+
      +
      +
      +
      +

      Compute log_probs

      +
      with torch.inference_mode():
+    emission, _ = model(waveform.to(device))
+    print(emission.shape)
+
      +
      +

      It should print:

      +
      torch.Size([1, 169, 28])
+
      +
      +
      +
      +

      Create token2id and id2token

      +
      token2id = bundle.get_dict(star=None)
+id2token = {i:t for t, i in token2id.items()}
+token2id["<eps>"] = 0
+del token2id["-"]
+
      +
      +
      +
      +

      Create word2id and id2word

      +
      words = list(set(transcript))
+word2id = dict()
+word2id['eps'] = 0
+for i, w in enumerate(words):
+  word2id[w] = i + 1
+
+id2word = {i:w for w, i in word2id.items()}
+
      +
      +

      Note that we only use words from the transcript of the test wave.

      +
      + +
      +

      Convert transcript to an FST graph

      +
      egs/librispeech/ASR/local/prepare_lang_fst.py --lang-dir ./
+
      +
      +

      The above command should generate two files H.fst and HL.fst. We will +use HL.fst below:

      +
      -rw-r--r-- 1 root root  13K Jun 12 08:28 H.fst
+-rw-r--r-- 1 root root 3.7K Jun 12 08:28 HL.fst
+
      +
      +
      +
      +

      Force aligner

      +

      Now, everything is ready. We can use the following code to get forced alignments.

      +
      from kaldi_decoder import DecodableCtc, FasterDecoder, FasterDecoderOptions
+import kaldifst
+
+def force_align():
+    HL = kaldifst.StdVectorFst.read("./HL.fst")
+    decodable = DecodableCtc(emission[0].contiguous().cpu().numpy())
+    decoder_opts = FasterDecoderOptions(max_active=3000)
+    decoder = FasterDecoder(HL, decoder_opts)
+    decoder.decode(decodable)
+    if not decoder.reached_final():
+        print(f"failed to decode xxx")
+        return None
+    ok, best_path = decoder.get_best_path()
+
+    (
+        ok,
+        isymbols_out,
+        osymbols_out,
+        total_weight,
+    ) = kaldifst.get_linear_symbol_sequence(best_path)
+    if not ok:
+        print(f"failed to get linear symbol sequence for xxx")
+        return None
+
+    # We need to use i-1 here since we have incremented tokens during
+    # HL construction
+    alignment = [i-1 for i in isymbols_out]
+    return alignment
+
+alignment = force_align()
+
+for i, a in enumerate(alignment):
+  print(i, id2token[a])
+
      +
      +

      The output should be identical to +https://pytorch.org/audio/main/tutorials/ctc_forced_alignment_api_tutorial.html#frame-level-alignments.

      +

      For ease of reference, we list the output below:

      +
      0 -
+1 -
+2 -
+3 -
+4 -
+5 -
+6 -
+7 -
+8 -
+9 -
+10 -
+11 -
+12 -
+13 -
+14 -
+15 -
+16 -
+17 -
+18 -
+19 -
+20 -
+21 -
+22 -
+23 -
+24 -
+25 -
+26 -
+27 -
+28 -
+29 -
+30 -
+31 -
+32 i
+33 -
+34 -
+35 h
+36 h
+37 a
+38 -
+39 -
+40 -
+41 d
+42 -
+43 -
+44 t
+45 h
+46 -
+47 a
+48 -
+49 -
+50 t
+51 -
+52 -
+53 -
+54 c
+55 -
+56 -
+57 -
+58 u
+59 u
+60 -
+61 -
+62 -
+63 r
+64 -
+65 i
+66 -
+67 -
+68 -
+69 -
+70 -
+71 -
+72 o
+73 -
+74 -
+75 -
+76 -
+77 -
+78 -
+79 s
+80 -
+81 -
+82 -
+83 i
+84 -
+85 t
+86 -
+87 -
+88 y
+89 -
+90 -
+91 -
+92 -
+93 b
+94 -
+95 e
+96 -
+97 -
+98 -
+99 -
+100 -
+101 s
+102 -
+103 -
+104 -
+105 -
+106 -
+107 -
+108 -
+109 -
+110 i
+111 -
+112 -
+113 d
+114 e
+115 -
+116 m
+117 -
+118 -
+119 e
+120 -
+121 -
+122 -
+123 -
+124 a
+125 -
+126 -
+127 t
+128 -
+129 t
+130 h
+131 -
+132 i
+133 -
+134 -
+135 -
+136 s
+137 -
+138 -
+139 -
+140 -
+141 m
+142 -
+143 -
+144 o
+145 -
+146 -
+147 -
+148 m
+149 -
+150 -
+151 e
+152 -
+153 n
+154 -
+155 t
+156 -
+157 -
+158 -
+159 -
+160 -
+161 -
+162 -
+163 -
+164 -
+165 -
+166 -
+167 -
+168 -
+
      +
      +

      To merge tokens, we use:

      +
      from icefall.ctc import merge_tokens
+token_spans = merge_tokens(alignment)
+for span in token_spans:
+  print(id2token[span.token], span.start, span.end)
+
      +
      +

      The output is given below:

      +
      i 32 33
+h 35 37
+a 37 38
+d 41 42
+t 44 45
+h 45 46
+a 47 48
+t 50 51
+c 54 55
+u 58 60
+r 63 64
+i 65 66
+o 72 73
+s 79 80
+i 83 84
+t 85 86
+y 88 89
+b 93 94
+e 95 96
+s 101 102
+i 110 111
+d 113 114
+e 114 115
+m 116 117
+e 119 120
+a 124 125
+t 127 128
+t 129 130
+h 130 131
+i 132 133
+s 136 137
+m 141 142
+o 144 145
+m 148 149
+e 151 152
+n 153 154
+t 155 156
+
      +
      +

      All of the code below is copied and modified +from https://pytorch.org/audio/main/tutorials/ctc_forced_alignment_api_tutorial.html.

      +
      +
      +

      Segment each word using the computed alignments

      +
      def unflatten(list_, lengths):
+    assert len(list_) == sum(lengths)
+    i = 0
+    ret = []
+    for l in lengths:
+        ret.append(list_[i : i + l])
+        i += l
+    return ret
+
+
+word_spans = unflatten(token_spans, [len(word) for word in transcript])
+print(word_spans)
+
      +
      +

      The output is:

      +
      [[TokenSpan(token=2, start=32, end=33)],
+ [TokenSpan(token=15, start=35, end=37), TokenSpan(token=1, start=37, end=38), TokenSpan(token=13, start=41, end=42)],
+ [TokenSpan(token=7, start=44, end=45), TokenSpan(token=15, start=45, end=46), TokenSpan(token=1, start=47, end=48), TokenSpan(token=7, start=50, end=51)],
+ [TokenSpan(token=20, start=54, end=55), TokenSpan(token=6, start=58, end=60), TokenSpan(token=9, start=63, end=64), TokenSpan(token=2, start=65, end=66), TokenSpan(token=5, start=72, end=73), TokenSpan(token=8, start=79, end=80), TokenSpan(token=2, start=83, end=84), TokenSpan(token=7, start=85, end=86), TokenSpan(token=16, start=88, end=89)],
+ [TokenSpan(token=17, start=93, end=94), TokenSpan(token=3, start=95, end=96), TokenSpan(token=8, start=101, end=102), TokenSpan(token=2, start=110, end=111), TokenSpan(token=13, start=113, end=114), TokenSpan(token=3, start=114, end=115)],
+ [TokenSpan(token=10, start=116, end=117), TokenSpan(token=3, start=119, end=120)],
+ [TokenSpan(token=1, start=124, end=125), TokenSpan(token=7, start=127, end=128)],
+ [TokenSpan(token=7, start=129, end=130), TokenSpan(token=15, start=130, end=131), TokenSpan(token=2, start=132, end=133), TokenSpan(token=8, start=136, end=137)],
+ [TokenSpan(token=10, start=141, end=142), TokenSpan(token=5, start=144, end=145), TokenSpan(token=10, start=148, end=149), TokenSpan(token=3, start=151, end=152), TokenSpan(token=4, start=153, end=154), TokenSpan(token=7, start=155, end=156)]
+]
+
      +
      +
      def preview_word(waveform, spans, num_frames, transcript, sample_rate=bundle.sample_rate):
+    ratio = waveform.size(1) / num_frames
+    x0 = int(ratio * spans[0].start)
+    x1 = int(ratio * spans[-1].end)
+    print(f"{transcript} {x0 / sample_rate:.3f} - {x1 / sample_rate:.3f} sec")
+    segment = waveform[:, x0:x1]
+    return IPython.display.Audio(segment.numpy(), rate=sample_rate)
+num_frames = emission.size(1)
+
      +
      +
      preview_word(waveform, word_spans[0], num_frames, transcript[0])
+preview_word(waveform, word_spans[1], num_frames, transcript[1])
+preview_word(waveform, word_spans[2], num_frames, transcript[2])
+preview_word(waveform, word_spans[3], num_frames, transcript[3])
+preview_word(waveform, word_spans[4], num_frames, transcript[4])
+preview_word(waveform, word_spans[5], num_frames, transcript[5])
+preview_word(waveform, word_spans[6], num_frames, transcript[6])
+preview_word(waveform, word_spans[7], num_frames, transcript[7])
+preview_word(waveform, word_spans[8], num_frames, transcript[8])
+
      +
      +

      The segmented wave of each word along with its time stamp is given below:

      + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
      WordTimeWave
      i0.644 - 0.664 sec + +
      had0.704 - 0.845 sec + +
      that0.885 - 1.026 sec + +
      curiosity1.086 - 1.790 sec + +
      beside1.871 - 2.314 sec + +
      me2.334 - 2.414 sec + +
      at2.495 - 2.575 sec + +
      this2.595 - 2.756 sec + +
      moment2.837 - 3.138 sec + +

      We repost the whole wave below for ease of reference:

      + + + + + + + + + + + +
      Wave filenameContentText
      Lab41-SRI-VOiCES-src-sp0307-ch127535-sg0042.wav + + + i had that curiosity beside me at this moment +
      +
      +

      Summary

      +

      Congratulations! You have succeeded in using the FST-based approach to +compute alignment of a test wave.

      +
      +
      + + +
      +
      + +
      +
      +
      +
      + + + + \ No newline at end of file diff --git a/genindex.html b/genindex.html index 68c5c180e..495feb476 100644 --- a/genindex.html +++ b/genindex.html @@ -48,6 +48,7 @@
    • Docker
    • Frequently Asked Questions (FAQs)
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      • +
    • FST-based forced alignment
    +
  • FST-based forced alignment +
    • diff --git a/installation/index.html b/installation/index.html index 88f151281..c8c709904 100644 --- a/installation/index.html +++ b/installation/index.html @@ -74,6 +74,7 @@
  • Docker
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  • Model export
    • +
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    • Recipes
      • diff --git a/model-export/export-model-state-dict.html b/model-export/export-model-state-dict.html index aed7e897b..662824d68 100644 --- a/model-export/export-model-state-dict.html +++ b/model-export/export-model-state-dict.html @@ -64,6 +64,7 @@
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    +
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    +
  • FST-based forced alignment

    • Caution

    -

    Please use a more recent version of PyTorch. For instance, torch 1.8 -may not work.

    +

    torch > 2.0 may not work. If you get errors while building pnnx, please switch +to torch < 2.0.

    1. Download the pre-trained model

    diff --git a/model-export/export-ncnn-lstm.html b/model-export/export-ncnn-lstm.html index e2e2f8a24..9c59f785b 100644 --- a/model-export/export-ncnn-lstm.html +++ b/model-export/export-ncnn-lstm.html @@ -21,7 +21,7 @@ - + @@ -72,6 +72,7 @@ +
  • FST-based forced alignment
    • Recipes
      • @@ -121,8 +122,8 @@

      Caution

      -

      Please use a more recent version of PyTorch. For instance, torch 1.8 -may not work.

      +

      torch > 2.0 may not work. If you get errors while building pnnx, please switch +to torch < 2.0.

      1. Download the pre-trained model

      @@ -806,7 +807,7 @@ with int8
      diff --git a/model-export/export-ncnn-zipformer.html b/model-export/export-ncnn-zipformer.html index 809384dd2..548516559 100644 --- a/model-export/export-ncnn-zipformer.html +++ b/model-export/export-ncnn-zipformer.html @@ -71,6 +71,7 @@
    +
  • FST-based forced alignment
    • Recipes
      • @@ -120,8 +121,8 @@

      Caution

      -

      Please use a more recent version of PyTorch. For instance, torch 1.8 -may not work.

      +

      torch > 2.0 may not work. If you get errors while building pnnx, please switch +to torch < 2.0.

      1. Download the pre-trained model

      diff --git a/model-export/export-ncnn.html b/model-export/export-ncnn.html index c588ec055..07154b153 100644 --- a/model-export/export-ncnn.html +++ b/model-export/export-ncnn.html @@ -63,6 +63,7 @@
    +
  • FST-based forced alignment
    • Recipes
      • diff --git a/model-export/export-onnx.html b/model-export/export-onnx.html index d84417fb6..7965543d8 100644 --- a/model-export/export-onnx.html +++ b/model-export/export-onnx.html @@ -65,6 +65,7 @@
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    +
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      • diff --git a/model-export/export-with-torch-jit-script.html b/model-export/export-with-torch-jit-script.html index 2cc3cb5d9..81beb80a4 100644 --- a/model-export/export-with-torch-jit-script.html +++ b/model-export/export-with-torch-jit-script.html @@ -63,6 +63,7 @@
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    +
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    • Export to ncnn
    +
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    • Recipes
      • diff --git a/objects.inv b/objects.inv index 0a9fba1de6b1aa6d190bb1760830f97599f59354..5233e738e39df59996984fd897212668240620ce 100644 GIT binary patch delta 2047 zcmVnS+n9+CsS(@ds^-V)ns++;V7wauixXk>}I%UCNZ zBIU$=`tk>oNQsiDwR=gt1n>a_K>#F`6e+nYiu;J>Dftg}^RlYPpvxkuk0j>~D-XpF%nO_d*KmSktQ|J0!p*L=zL%yKNYhXZzPHtBpN*j_>Tn7Y z;sKN6uBeWr!hgpirMpKgyoAZmvZy#oG0COFDlAwqJCLf3ah~M8U=V^N1DTcnPjO)j zUkAW1=(lsUR+`j$G@1}-LV_6?ntC=ePD=b5bP11D$$=#mEjiXkJkS`IdH(Y$2EIN= zSBeK2_!SVLfU`K?*ZX)+v>9ym@qg3ht+^Z??0S8ap!C7*W`nj> zoU^n}NHwQ#u>L;$TH_^fBFTvqr(o3cWpjEf!jhtr(*C+z8JO`JbtrP?{SZ-{* zt67tw{7kR~wH=`zLjcydq)6^bjsW7oee+}Y(pI+ zf1p*7%XAts!s~KenZb$`>xk9?AG+#BkQy1?j0NdRrzo|J73aC&JlC8rUVlznA#6^_Vk4B4j}3~kH6fB&H)^u3 zbU19XNbk;w7tzy-43O>8(dmIKAPcV21E!18_;3Z>sN;rAC`+&^NH7V(r>qp665}Fs z=pS*OMo+6hK$ZcgSO8xC5b(OZy*d|ZLlKlwNDy>g0_!ryk5Bfj1hI^g1M`0T7;e|| z{(oz`#^_&KK2s>PNS4)If7;ULC(#O_e;T2QIql-_%uMp~H?*D=`Cf~3lRct0>|XRa zpKHQomSyP0yEmWN%V6hfPVEU#HMeJ4UbSsTYwT`$r^VyBIh!y(u&(;{zg;=7j9z9+S-gkbhB==q*4qB*$<~9u2=%$x=sL;3_d=P*w@* z3eApjd>1p(w+rS({1<0*uY4emV!NG%Hq8paKME9=WmUwG6Q-ofqkL z+4jqz=RN~&4BXIyHyBWH7S$3Pw>@uKHKNJ>gLN2YkO0a!0z}&gAgV-aO$%y&BXn)> z1w$d*1Sg~okrip*wVvlx(eRC(RDV^I@cefPT{E)y3ZdV&Jo6=jX_?pEfeT5GiM8Pb zr!=vOhz~zhJ);RfSHwsXMe99g{hXe5FZ6@nuc!5acXYuETk^@J@iL)ZL(e95z6JGC zYd5T?^`0*9qKV!K10+v>Wj}2;Ujy&s=bVP|GppZ;)%OUx(2nld>UC(f5Pt`Gc1Sf_ zJ3THT9Ob=y#0{a9sYUCq3w>VX#u>@DRgcQ578Tb#EUBHI5ynorq@RG(SuMB34Na-(Qw+_to@fid+NpWHn1gE*U5u4(4=PIMkzXj#Q+TyoJ7>|h{*0dt|eg`hKq zQM9XyBZ}p7MBD3hyV3|8kbh=F>m)+N>XK9sRCIrxLT9cxLELR|Co*OH(A+_YyE!_g zSZTFgWMdBlFs7nFXPG$6=5Y2;c;Zs(DcU}6z}zmj7arP2r#d5|wZV{wo;LC$Ydb7Z z>q3BQ>vqQ(J=i=TPxD;g5-oPTB?k~!`81r9eHEr?>8O@B=k#K14Joe*53 z;>!YT>=AUi5rZdGbZO$L)bqZ6dv&|@G-tsQ48-8UMh=H+ICyf2S#HT(y_&8(emYLr z9i!sC9fJKMr@I&xFwvbjjoq_*S}wO|1V~%f6$D`!dMk-26x78=9*^`XrU~)BLF@JP zgcY0~DPI!Lh5#(e^?&+$JF7vjCf!%He>Aux%|M`k-2OEa2`0p%NOTA;i@`_2@?wc& z4FRp`Z^AaktOjY~5(Sdqn29-wTRF4!xXmVAL+Ft{fEk+E5xeeT{UnT=Srq5$j>LXi zZ#9R06*3&Fj_%6XXYZbveZV_z#}l<%$g9SLr`DHoK^5Xq6MuaBtkcoJLfPp!+O6=p zDq#3OSHI9BAD`+%7eYU=JAAj@hU0P55@B0yLrX6E2D%?8XJhgflg9aSy$$ilKYOtz znI95PjcD3-UACXO?O(Pu^QEv`+qt@;p1bP4FmMXX+PL7*q92)#Rr-pjX(>)b9oJ?Oybs d|HYyAHNoO%2j|xY&;0f@GmYWkT-C@;*$9ggk~-O{%DlSXvf{vgEfcx+59F zKckw&X+`L2Qx=%AnnZb89Pr!i?J7&}>XcT5#K%>VQkG_!w0}Q8vOGJB0Ut|ztm9vR z2uTsrL_u5@tWNJ5#+|GL1o!!r5lJa2V?oHFBE6#bWtNpc5IYhiL`aHEQ~~3H<6{cv zLK{V_M3-gU39kP z;sKLHZ1xRl?-4Dr-g(ObW4JHRdXq9!XtAxQL5EFmOYXfz3+&C$+GF zuL9sF^y@iVOHcB7w4M-nLR=YXo(3E#BNcuFzW9pO@sY-LS~09hJfsn>isJD(1mB#a zE2)Dte1(BHJ`(srLO5L!^FLB{#934vnnQFTiUwVM{C{+Lt3M78b@hCtp;&|4%?9o2 zsGvy`lX}kI==uBTE033;iG(JSnp{!Mm(KaE360BISo{0#Y+%M~)T4-*`_GylBeAjZ zzM*Y~@}poHDjr)kh5&5Hq>S%LZCPh+9M)5g^aiXhtpy5s}sMoYJ24sD3IeS?O}XA95<+t>#w z9@4rjL^=&AVNKhLk|Sdttd7!Ra;~k0!)phygFgbuI4TRtfRu>^ ze@i(pm0f1jmDQ}xSdgw%ijvz{YMyh=bEWy}^?xJ_&N(KFNk}Un4pOnB5Q?lDwpmv! zIBc>|?aq*u;q!_Nh|SW``++PV3a<16)Ol%qxB_j|YeOa!C0G_Dm;~38EP1Cy8P6Q* zM^q%?^Xd9~ zb$?xB_-|UhP$+ar2J5~*?d0>5=#0=mt!PO}ZMzNrXK-u|2X3+Fd3n4TBO#na`eDPK$A;v?j|;FZ99Sboh0CJ|!+6_u z>Q=Pk|2i?1bVLxT@gP+XFjQTaxh|vD?F_UrXhSF0VnE_rl#)!`?$~0anl_1yo`1tQ z5CfK%d_7Z8^6UOnMf(AhM+15|~+C=0FC)-Gqms&072)q8Ha z(=$TpDR;dSa4M@6hPh^nm8qFpTP8PBrpIk3`x@GaK zJEuBhqP)S7ho3I;Lt{HEaDQtg5_x@sd zD~E$;mzd>D=IYgS?OiCZzVwVSgpR}gAs2%v{% zmSfv>o}Yzw!H1$k-hYvpZ)iKuTfYjqbyY`uWgM#aZz_Gjdu_{`PP>s;joF@BU)+W= z#Gxkm_F1Q+0YlO0SlX@aOI5(_|6KjH9{KpxCA1+76T4;YcDt=RZaN`stKHVH%f5l{ zhm=v5y`iLVzFhCN_~RdgSQE+*F}t5c>l8GkFBiK%I~3Docker
    • Frequently Asked Questions (FAQs)
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      • +
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        diff --git a/recipes/Finetune/from_supervised/finetune_zipformer.html b/recipes/Finetune/from_supervised/finetune_zipformer.html index b7120b746..6eee87c57 100644 --- a/recipes/Finetune/from_supervised/finetune_zipformer.html +++ b/recipes/Finetune/from_supervised/finetune_zipformer.html @@ -51,6 +51,7 @@
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                • Recipes
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                      diff --git a/recipes/Non-streaming-ASR/librispeech/conformer_ctc.html b/recipes/Non-streaming-ASR/librispeech/conformer_ctc.html index 96e47b371..e0fb5c97f 100644 --- a/recipes/Non-streaming-ASR/librispeech/conformer_ctc.html +++ b/recipes/Non-streaming-ASR/librispeech/conformer_ctc.html @@ -51,6 +51,7 @@
                    • Docker
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                            diff --git a/recipes/Non-streaming-ASR/librispeech/pruned_transducer_stateless.html b/recipes/Non-streaming-ASR/librispeech/pruned_transducer_stateless.html index 0d1214529..6f7940f1c 100644 --- a/recipes/Non-streaming-ASR/librispeech/pruned_transducer_stateless.html +++ b/recipes/Non-streaming-ASR/librispeech/pruned_transducer_stateless.html @@ -51,6 +51,7 @@
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                              diff --git a/recipes/Non-streaming-ASR/librispeech/tdnn_lstm_ctc.html b/recipes/Non-streaming-ASR/librispeech/tdnn_lstm_ctc.html index 98af9e8e2..259438a87 100644 --- a/recipes/Non-streaming-ASR/librispeech/tdnn_lstm_ctc.html +++ b/recipes/Non-streaming-ASR/librispeech/tdnn_lstm_ctc.html @@ -51,6 +51,7 @@
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                                diff --git a/recipes/Non-streaming-ASR/librispeech/zipformer_ctc_blankskip.html b/recipes/Non-streaming-ASR/librispeech/zipformer_ctc_blankskip.html index 983d90ace..2cbd3b2aa 100644 --- a/recipes/Non-streaming-ASR/librispeech/zipformer_ctc_blankskip.html +++ b/recipes/Non-streaming-ASR/librispeech/zipformer_ctc_blankskip.html @@ -51,6 +51,7 @@
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                                  diff --git a/recipes/Non-streaming-ASR/librispeech/zipformer_mmi.html b/recipes/Non-streaming-ASR/librispeech/zipformer_mmi.html index d78b98a8e..0327d1909 100644 --- a/recipes/Non-streaming-ASR/librispeech/zipformer_mmi.html +++ b/recipes/Non-streaming-ASR/librispeech/zipformer_mmi.html @@ -51,6 +51,7 @@
                                • Docker
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                                                        diff --git a/recipes/Streaming-ASR/librispeech/lstm_pruned_stateless_transducer.html b/recipes/Streaming-ASR/librispeech/lstm_pruned_stateless_transducer.html index 5b7b37dc5..ab15498ea 100644 --- a/recipes/Streaming-ASR/librispeech/lstm_pruned_stateless_transducer.html +++ b/recipes/Streaming-ASR/librispeech/lstm_pruned_stateless_transducer.html @@ -51,6 +51,7 @@
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                                                          diff --git a/recipes/Streaming-ASR/librispeech/pruned_transducer_stateless.html b/recipes/Streaming-ASR/librispeech/pruned_transducer_stateless.html index 82fef8315..3fb74f38a 100644 --- a/recipes/Streaming-ASR/librispeech/pruned_transducer_stateless.html +++ b/recipes/Streaming-ASR/librispeech/pruned_transducer_stateless.html @@ -51,6 +51,7 @@
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                                                            diff --git a/recipes/Streaming-ASR/librispeech/zipformer_transducer.html b/recipes/Streaming-ASR/librispeech/zipformer_transducer.html index 899786d75..4f47ef222 100644 --- a/recipes/Streaming-ASR/librispeech/zipformer_transducer.html +++ b/recipes/Streaming-ASR/librispeech/zipformer_transducer.html @@ -51,6 +51,7 @@
                                                          • Docker
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                                                                      • diff --git a/searchindex.js b/searchindex.js index 354ea262e..b95a7ae67 100644 --- a/searchindex.js +++ b/searchindex.js @@ -1 +1 @@ -Search.setIndex({"docnames": ["contributing/code-style", "contributing/doc", "contributing/how-to-create-a-recipe", "contributing/index", "decoding-with-langugage-models/LODR", "decoding-with-langugage-models/index", "decoding-with-langugage-models/rescoring", "decoding-with-langugage-models/shallow-fusion", "docker/index", "docker/intro", "faqs", "for-dummies/data-preparation", "for-dummies/decoding", "for-dummies/environment-setup", "for-dummies/index", "for-dummies/model-export", "for-dummies/training", "huggingface/index", "huggingface/pretrained-models", "huggingface/spaces", "index", "installation/index", "model-export/export-model-state-dict", "model-export/export-ncnn", "model-export/export-ncnn-conv-emformer", "model-export/export-ncnn-lstm", 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of LM rescoring + LODR with different beam sizes": [[6, "id2"]], "LM-rescoring-based methods vs shallow-fusion-based methods (The numbers in each field is WER on test-clean, WER on test-other and decoding time on test-clean)": [[6, "id3"]], "Shallow fusion for Transducer": [[7, "shallow-fusion-for-transducer"]], "WERs and decoding time (on test-clean) of shallow fusion with different beam sizes": [[7, "id2"]], "Docker": [[8, "docker"]], "Introduction": [[9, "introduction"], [54, "introduction"]], "View available tags": [[9, "view-available-tags"]], "CUDA-enabled docker images": [[9, "cuda-enabled-docker-images"]], "CPU-only docker images": [[9, "cpu-only-docker-images"]], "Download a docker image (CUDA)": [[9, "download-a-docker-image-cuda"]], "Download a docker image (CPU)": [[9, "download-a-docker-image-cpu"]], "Run a docker image with GPU": [[9, "run-a-docker-image-with-gpu"]], "Run a docker image with CPU": [[9, "run-a-docker-image-with-cpu"]], "Run yesno within a docker container": [[9, "run-yesno-within-a-docker-container"]], "Update the code": [[9, "update-the-code"]], "Data preparation": [[9, "data-preparation"], [21, "data-preparation"], [31, "data-preparation"], [32, "data-preparation"], [34, "data-preparation"], [37, "data-preparation"], [39, "data-preparation"], [40, "data-preparation"], [42, "data-preparation"], [43, "data-preparation"], [44, "data-preparation"], [45, "data-preparation"], [47, "data-preparation"], [48, "data-preparation"], [50, "data-preparation"], [56, "data-preparation"], [57, "data-preparation"], [58, "data-preparation"], [60, "data-preparation"], [61, "data-preparation"]], "Frequently Asked Questions (FAQs)": [[10, "frequently-asked-questions-faqs"]], "OSError: libtorch_hip.so: cannot open shared object file: no such file or directory": [[10, "oserror-libtorch-hip-so-cannot-open-shared-object-file-no-such-file-or-directory"]], "AttributeError: module \u2018distutils\u2019 has no attribute \u2018version\u2019": [[10, "attributeerror-module-distutils-has-no-attribute-version"]], "ImportError: libpython3.10.so.1.0: cannot open shared object file: No such file or directory": [[10, "importerror-libpython3-10-so-1-0-cannot-open-shared-object-file-no-such-file-or-directory"]], "For the more curious": [[11, "for-the-more-curious"], [12, "for-the-more-curious"], [13, "for-the-more-curious"], [15, "for-the-more-curious"], [16, "for-the-more-curious"]], "A quick look to the generated files": [[11, "a-quick-look-to-the-generated-files"]], "download": [[11, "download"]], "data": [[11, "data"]], "Environment setup": [[13, "environment-setup"]], "Create a virtual environment": [[13, "create-a-virtual-environment"]], "Install dependencies": [[13, "install-dependencies"]], "Install icefall": [[13, "install-icefall"]], "Icefall for dummies tutorial": [[14, "icefall-for-dummies-tutorial"]], "Model Export": [[15, "model-export"]], "Export the model parameters via model.state_dict()": [[15, "export-the-model-parameters-via-model-state-dict"]], "Export via torch.jit.script()": [[15, "export-via-torch-jit-script"]], "Export via torch.onnx.export()": [[15, "export-via-torch-onnx-export"]], "Huggingface": [[17, "huggingface"]], "Pre-trained models": [[18, "pre-trained-models"]], "Huggingface spaces": [[19, "huggingface-spaces"]], "YouTube Video": [[19, "youtube-video"], [21, "youtube-video"]], "Icefall": [[20, "icefall"]], "Contents:": [[20, null]], "Installation": [[21, "installation"]], "(0) Install CUDA toolkit and cuDNN": [[21, "install-cuda-toolkit-and-cudnn"]], "(1) Install torch and torchaudio": [[21, "install-torch-and-torchaudio"]], "(2) Install k2": [[21, "install-k2"]], "(3) Install lhotse": [[21, "install-lhotse"]], "(4) Download icefall": [[21, "download-icefall"]], "Installation example": [[21, "installation-example"]], "(1) Create a virtual environment": [[21, "create-a-virtual-environment"]], "(2) Install CUDA toolkit and cuDNN": [[21, "id1"]], "(3) Install torch and torchaudio": [[21, "id2"]], "(4) Install k2": [[21, "id3"]], "(5) Install lhotse": [[21, "id5"]], "(6) Download icefall": [[21, "id6"]], "Test Your Installation": [[21, "test-your-installation"]], "Export model.state_dict()": [[22, "export-model-state-dict"], [42, "export-model-state-dict"], [44, "export-model-state-dict"], [45, "export-model-state-dict"], [56, "export-model-state-dict"], [57, "export-model-state-dict"], [58, "export-model-state-dict"]], "When to use it": [[22, "when-to-use-it"], [28, "when-to-use-it"], [29, "when-to-use-it"]], "How to export": [[22, "how-to-export"], [28, "how-to-export"], [29, "how-to-export"]], "How to use the exported model": [[22, "how-to-use-the-exported-model"], [28, "how-to-use-the-exported-model"]], "Use the exported model to run decode.py": [[22, "use-the-exported-model-to-run-decode-py"]], "Export to ncnn": [[23, "export-to-ncnn"]], "Export ConvEmformer transducer models to ncnn": [[24, 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"decoding"], [25, "decoding"], [35, "decoding"], [38, "decoding"], [40, "decoding"], [41, "decoding"], [43, "decoding"], [44, "decoding"], [46, "decoding"], [47, "decoding"], [48, "decoding"], [49, "decoding"], [51, "decoding"], [52, "decoding"], [54, "decoding"], [60, "decoding"], [61, "decoding"], [62, "decoding"]], "Pre-trained model": [[2, "pre-trained-model"]], "Contributing": [[3, "contributing"]], "LODR for RNN Transducer": [[4, "lodr-for-rnn-transducer"]], "WER of LODR with different beam sizes": [[4, "id1"]], "Decoding with language models": [[5, "decoding-with-language-models"]], "LM rescoring for Transducer": [[6, "lm-rescoring-for-transducer"]], "WERs of LM rescoring with different beam sizes": [[6, "id1"]], "WERs of LM rescoring + LODR with different beam sizes": [[6, "id2"]], "LM-rescoring-based methods vs shallow-fusion-based methods (The numbers in each field is WER on test-clean, WER on test-other and decoding time on test-clean)": [[6, "id3"]], "Shallow fusion for Transducer": [[7, "shallow-fusion-for-transducer"]], "WERs and decoding time (on test-clean) of shallow fusion with different beam sizes": [[7, "id2"]], "Docker": [[8, "docker"]], "Introduction": [[9, "introduction"], [58, "introduction"]], "View available tags": [[9, "view-available-tags"]], "CUDA-enabled docker images": [[9, "cuda-enabled-docker-images"]], "CPU-only docker images": [[9, "cpu-only-docker-images"]], "Download a docker image (CUDA)": [[9, "download-a-docker-image-cuda"]], "Download a docker image (CPU)": [[9, "download-a-docker-image-cpu"]], "Run a docker image with GPU": [[9, "run-a-docker-image-with-gpu"]], "Run a docker image with CPU": [[9, "run-a-docker-image-with-cpu"]], "Run yesno within a docker container": [[9, "run-yesno-within-a-docker-container"]], "Update the code": [[9, "update-the-code"]], "Data preparation": [[9, "data-preparation"], [25, "data-preparation"], [35, "data-preparation"], [36, "data-preparation"], [38, "data-preparation"], [41, "data-preparation"], [43, "data-preparation"], [44, "data-preparation"], [46, "data-preparation"], [47, "data-preparation"], [48, "data-preparation"], [49, "data-preparation"], [51, "data-preparation"], [52, "data-preparation"], [54, "data-preparation"], [60, "data-preparation"], [61, "data-preparation"], [62, "data-preparation"], [64, "data-preparation"], [65, "data-preparation"]], "Frequently Asked Questions (FAQs)": [[10, "frequently-asked-questions-faqs"]], "OSError: libtorch_hip.so: cannot open shared object file: no such file or directory": [[10, "oserror-libtorch-hip-so-cannot-open-shared-object-file-no-such-file-or-directory"]], "AttributeError: module \u2018distutils\u2019 has no attribute \u2018version\u2019": [[10, "attributeerror-module-distutils-has-no-attribute-version"]], "ImportError: libpython3.10.so.1.0: cannot open shared object file: No such file or directory": [[10, "importerror-libpython3-10-so-1-0-cannot-open-shared-object-file-no-such-file-or-directory"]], "For the more curious": [[11, "for-the-more-curious"], [12, "for-the-more-curious"], [13, "for-the-more-curious"], [15, "for-the-more-curious"], [16, "for-the-more-curious"]], "A quick look to the generated files": [[11, "a-quick-look-to-the-generated-files"]], "download": [[11, "download"]], "data": [[11, "data"]], "Environment setup": [[13, "environment-setup"]], "Create a virtual environment": [[13, "create-a-virtual-environment"]], "Install dependencies": [[13, "install-dependencies"]], "Install icefall": [[13, "install-icefall"]], "Icefall for dummies tutorial": [[14, "icefall-for-dummies-tutorial"]], "Model Export": [[15, "model-export"]], "Export the model parameters via model.state_dict()": [[15, "export-the-model-parameters-via-model-state-dict"]], "Export via torch.jit.script()": [[15, "export-via-torch-jit-script"]], "Export via torch.onnx.export()": [[15, "export-via-torch-onnx-export"]], "Two approaches": [[17, "two-approaches"]], "Differences between the two approaches": [[17, "differences-between-the-two-approaches"]], "FST-based forced alignment": [[18, "fst-based-forced-alignment"]], "Contents:": [[18, null], [24, null]], "k2-based forced alignment": [[19, "k2-based-forced-alignment"]], "Kaldi-based forced alignment": [[20, "kaldi-based-forced-alignment"]], "Prepare the environment": [[20, "prepare-the-environment"]], "Get the test data": [[20, "get-the-test-data"]], "Compute log_probs": [[20, "compute-log-probs"]], "Create token2id and id2token": [[20, "create-token2id-and-id2token"]], "Create word2id and id2word": [[20, "create-word2id-and-id2word"]], "Generate lexicon-related files": [[20, "generate-lexicon-related-files"]], "Convert transcript to an FST graph": [[20, "convert-transcript-to-an-fst-graph"]], "Force aligner": [[20, "force-aligner"]], "Segment each word using the computed alignments": [[20, "segment-each-word-using-the-computed-alignments"]], "Summary": [[20, "summary"]], "Huggingface": [[21, "huggingface"]], "Pre-trained models": [[22, "pre-trained-models"]], "Huggingface spaces": [[23, "huggingface-spaces"]], "YouTube Video": [[23, "youtube-video"], [25, "youtube-video"]], "Icefall": [[24, "icefall"]], "Installation": [[25, "installation"]], "(0) Install CUDA toolkit and cuDNN": [[25, "install-cuda-toolkit-and-cudnn"]], "(1) Install torch and torchaudio": [[25, "install-torch-and-torchaudio"]], "(2) Install k2": [[25, "install-k2"]], "(3) Install lhotse": [[25, "install-lhotse"]], "(4) Download icefall": [[25, "download-icefall"]], "Installation example": [[25, "installation-example"]], "(1) Create a virtual environment": [[25, "create-a-virtual-environment"]], "(2) Install CUDA toolkit and cuDNN": [[25, "id1"]], "(3) Install torch and torchaudio": [[25, "id2"]], "(4) Install k2": [[25, "id3"]], "(5) Install lhotse": [[25, "id5"]], "(6) Download icefall": [[25, "id6"]], "Test Your Installation": [[25, "test-your-installation"]], "Export model.state_dict()": [[26, "export-model-state-dict"], [46, "export-model-state-dict"], [48, "export-model-state-dict"], [49, "export-model-state-dict"], [60, "export-model-state-dict"], [61, "export-model-state-dict"], [62, "export-model-state-dict"]], "When to use it": [[26, "when-to-use-it"], [32, "when-to-use-it"], [33, "when-to-use-it"]], "How to export": [[26, "how-to-export"], [32, "how-to-export"], [33, "how-to-export"]], "How to use the exported model": [[26, "how-to-use-the-exported-model"], [32, "how-to-use-the-exported-model"]], "Use the exported model to run decode.py": [[26, "use-the-exported-model-to-run-decode-py"]], "Export to ncnn": [[27, "export-to-ncnn"]], "Export ConvEmformer transducer models to ncnn": [[28, "export-convemformer-transducer-models-to-ncnn"]], "1. 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Modify the exported encoder for sherpa-ncnn": [[28, "modify-the-exported-encoder-for-sherpa-ncnn"], [29, "modify-the-exported-encoder-for-sherpa-ncnn"], [30, "modify-the-exported-encoder-for-sherpa-ncnn"]], "7. (Optional) int8 quantization with sherpa-ncnn": [[28, "optional-int8-quantization-with-sherpa-ncnn"], [29, "optional-int8-quantization-with-sherpa-ncnn"]], "Export LSTM transducer models to ncnn": [[29, "export-lstm-transducer-models-to-ncnn"]], "Export streaming Zipformer transducer models to ncnn": [[30, "export-streaming-zipformer-transducer-models-to-ncnn"]], "Export to ONNX": [[31, "export-to-onnx"]], "sherpa-onnx": [[31, "sherpa-onnx"]], "Example": [[31, "example"]], "Download the pre-trained model": [[31, "download-the-pre-trained-model"], [38, "download-the-pre-trained-model"], [40, "download-the-pre-trained-model"], [41, "download-the-pre-trained-model"], [43, "download-the-pre-trained-model"], [47, "download-the-pre-trained-model"], [51, "download-the-pre-trained-model"], [52, "download-the-pre-trained-model"], [54, "download-the-pre-trained-model"]], "Export the model to ONNX": [[31, "export-the-model-to-onnx"]], "Decode sound files with exported ONNX models": [[31, 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"Configurable options": [[38, "configurable-options"], [41, "configurable-options"], [43, "configurable-options"], [46, "configurable-options"], [48, "configurable-options"], [49, "configurable-options"], [60, "configurable-options"], [61, "configurable-options"], [62, "configurable-options"]], "Pre-configured options": [[38, "pre-configured-options"], [41, "pre-configured-options"], [43, "pre-configured-options"], [46, "pre-configured-options"], [48, "pre-configured-options"], [49, "pre-configured-options"], [60, "pre-configured-options"], [61, "pre-configured-options"], [62, "pre-configured-options"]], "Training logs": [[38, "training-logs"], [40, "training-logs"], [41, "training-logs"], [43, "training-logs"], [46, "training-logs"], [48, "training-logs"], [49, "training-logs"], [60, "training-logs"], [61, "training-logs"], [62, "training-logs"]], "Usage examples": [[38, "usage-examples"], [40, "usage-examples"], [41, "usage-examples"], [43, "usage-examples"]], "Case 1": [[38, 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"export-model-using-torch-jit-script"]], "Download pretrained models": [[46, "download-pretrained-models"], [48, "download-pretrained-models"], [49, "download-pretrained-models"], [60, "download-pretrained-models"], [61, "download-pretrained-models"], [62, "download-pretrained-models"], [64, "download-pretrained-models"], [65, "download-pretrained-models"]], "Deploy with Sherpa": [[46, "deploy-with-sherpa"], [61, "deploy-with-sherpa"], [62, "deploy-with-sherpa"]], "TDNN-LSTM-CTC": [[47, "tdnn-lstm-ctc"], [52, "tdnn-lstm-ctc"]], "Inference with a pre-trained model": [[47, "inference-with-a-pre-trained-model"], [51, "inference-with-a-pre-trained-model"], [52, "inference-with-a-pre-trained-model"], [54, "inference-with-a-pre-trained-model"]], "Zipformer CTC Blank Skip": [[48, "zipformer-ctc-blank-skip"]], "Export models": [[48, "export-models"], [49, "export-models"], [60, "export-models"], [64, "export-models"], [65, "export-models"]], "Zipformer MMI": [[49, "zipformer-mmi"]], "TIMIT": [[50, "timit"]], "TDNN-LiGRU-CTC": [[51, "tdnn-ligru-ctc"]], "YesNo": [[53, "yesno"]], "TDNN-CTC": [[54, "tdnn-ctc"]], "Download kaldifeat": [[54, "download-kaldifeat"]], "RNN-LM": [[55, "rnn-lm"]], "Train an RNN language model": [[56, "train-an-rnn-language-model"]], "Streaming ASR": [[57, "streaming-asr"]], "Streaming Conformer": [[58, "streaming-conformer"]], "Streaming Emformer": [[58, "streaming-emformer"]], "LSTM Transducer": [[60, "lstm-transducer"]], "Which model to use": [[60, "which-model-to-use"]], "Export model using torch.jit.trace()": [[60, "export-model-using-torch-jit-trace"], [62, "export-model-using-torch-jit-trace"]], "Simulate streaming decoding": [[61, "simulate-streaming-decoding"], [62, "simulate-streaming-decoding"]], "Real streaming decoding": [[61, "real-streaming-decoding"], [62, "real-streaming-decoding"]], "Zipformer Transducer": [[62, "zipformer-transducer"]], "TTS": [[63, "tts"]], "VITS-LJSpeech": [[64, "vits-ljspeech"]], "Install extra dependencies": [[64, "install-extra-dependencies"]], "Build Monotonic Alignment Search": [[64, "build-monotonic-alignment-search"], [65, "build-monotonic-alignment-search"]], "Inference": [[64, "inference"], [65, "inference"]], "Usage in sherpa-onnx": [[64, "usage-in-sherpa-onnx"]], "Install sherpa-onnx": [[64, "install-sherpa-onnx"]], "Download lexicon files": [[64, "download-lexicon-files"]], "Run sherpa-onnx": [[64, "run-sherpa-onnx"]], "VITS-VCTK": [[65, "vits-vctk"]], "Recipes": [[66, "recipes"]]}, "indexentries": {}}) \ No newline at end of file
  • Docker