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fix(kws): honor optional output directory on every call
Signed-off-by: zhifu gao <[email protected]>
This commit is contained in:
zhifu gao
@@ -0,0 +1,44 @@
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name: Validate KWS output handling
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on:
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pull_request:
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paths:
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- "funasr/models/fsmn_kws/**"
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- "funasr/models/fsmn_kws_mt/**"
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- "funasr/models/sanm_kws/**"
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- "funasr/models/sanm_kws_streaming/**"
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- "funasr/utils/datadir_writer.py"
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- "funasr/utils/kws_utils.py"
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- "tests/test_kws_optional_output.py"
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- ".github/workflows/test-kws-output.yml"
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push:
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branches: [main]
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paths:
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- "funasr/models/fsmn_kws/**"
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- "funasr/models/fsmn_kws_mt/**"
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- "funasr/models/sanm_kws/**"
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- "funasr/models/sanm_kws_streaming/**"
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- "funasr/utils/datadir_writer.py"
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- "funasr/utils/kws_utils.py"
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- "tests/test_kws_optional_output.py"
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- ".github/workflows/test-kws-output.yml"
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permissions:
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contents: read
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jobs:
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kws-output:
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runs-on: ubuntu-latest
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timeout-minutes: 15
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steps:
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- uses: actions/checkout@v4
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- uses: actions/setup-python@v5
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with:
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python-version: "3.11"
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cache: pip
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- name: Install CPU test dependencies
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run: |
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python -m pip install torch --index-url https://download.pytorch.org/whl/cpu
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python -m pip install "numpy<2" "kaldiio>=2.17.0" librosa "rapidfuzz>=3.0.0" six
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- name: Check KWS results and optional file output without model weights
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run: python -m unittest discover -s tests -p test_kws_optional_output.py -v
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@@ -263,12 +263,13 @@ class FsmnKWS(torch.nn.Module):
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is_deted, det_keyword, det_score = detect_result[0], detect_result[1], detect_result[2]
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if is_deted:
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self.writer["detect"][key[i]] = "detected " + det_keyword + " " + str(det_score)
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det_info = "detected " + det_keyword + " " + str(det_score)
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else:
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self.writer["detect"][key[i]] = "rejected"
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det_info = "rejected"
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if kwargs.get("output_dir") is not None:
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self.writer["detect"][key[i]] = det_info
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result_i = {"key": key[i], "text": det_info}
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results.append(result_i)
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@@ -325,23 +325,25 @@ class FsmnKWSMT(torch.nn.Module):
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is_deted, det_keyword, det_score = detect_result[0], detect_result[1], detect_result[2]
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if is_deted:
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self.writer["detect"][key[i]] = "detected " + det_keyword + " " + str(det_score)
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det_info = "detected " + det_keyword + " " + str(det_score)
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else:
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self.writer["detect"][key[i]] = "rejected"
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det_info = "rejected"
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if kwargs.get("output_dir") is not None:
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self.writer["detect"][key[i]] = det_info
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x2 = encoder_out2[i, :encoder_out_lens[i], :]
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detect_result2 = self.kws_decoder2.decode(x2)
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is_deted2, det_keyword2, det_score2 = detect_result2[0], detect_result2[1], detect_result2[2]
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if is_deted2:
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self.writer["detect2"][key[i]] = "detected " + det_keyword2 + " " + str(det_score2)
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det_info2 = "detected " + det_keyword2 + " " + str(det_score2)
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else:
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self.writer["detect2"][key[i]] = "rejected"
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det_info2 = "rejected"
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if kwargs.get("output_dir") is not None:
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self.writer["detect2"][key[i]] = det_info2
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result_i = {"key": key[i], "text": det_info, "text2": det_info2}
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results.append(result_i)
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@@ -286,12 +286,13 @@ class SanmKWS(torch.nn.Module):
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is_deted, det_keyword, det_score = detect_result[0], detect_result[1], detect_result[2]
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if is_deted:
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self.writer["detect"][key[i]] = "detected " + det_keyword + " " + str(det_score)
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det_info = "detected " + det_keyword + " " + str(det_score)
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else:
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self.writer["detect"][key[i]] = "rejected"
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det_info = "rejected"
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if kwargs.get("output_dir") is not None:
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self.writer["detect"][key[i]] = det_info
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result_i = {"key": key[i], "text": det_info}
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results.append(result_i)
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@@ -271,12 +271,13 @@ class SanmKWSStreaming(SanmKWS):
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is_deted, det_keyword, det_score = detect_result[0], detect_result[1], detect_result[2]
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if is_deted:
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self.writer["detect"][key[i]] = "detected " + det_keyword + " " + str(det_score)
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det_info = "detected " + det_keyword + " " + str(det_score)
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else:
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self.writer["detect"][key[i]] = "rejected"
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det_info = "rejected"
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if kwargs.get("output_dir") is not None:
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self.writer["detect"][key[i]] = det_info
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result_i = {"key": key[i], "text": det_info}
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results.append(result_i)
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@@ -0,0 +1,194 @@
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"""Exercise production KWS output handling without loading acoustic models."""
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import itertools
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import tempfile
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import unittest
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from pathlib import Path
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from types import SimpleNamespace
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from unittest.mock import patch
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import torch
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from funasr.models.fsmn_kws.model import FsmnKWS
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from funasr.models.fsmn_kws_mt.model import FsmnKWSMT
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from funasr.models.sanm_kws.model import SanmKWS
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from funasr.models.sanm_kws_streaming.model import SanmKWSStreaming
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class FixedDecoder:
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def __init__(self, ctc, keywords=None, token_list=None, seg_dict=None):
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self.result = ctc
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def decode(self, encoder_out):
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return self.result
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VARIANTS = (FsmnKWS, FsmnKWSMT, SanmKWS, SanmKWSStreaming)
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OMITTED = object()
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class KwsOptionalOutputTest(unittest.TestCase):
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def setUp(self):
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self.directory = tempfile.TemporaryDirectory()
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self.addCleanup(self.directory.cleanup)
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self.patch_decoder = patch(
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"funasr.utils.kws_utils.KwsCtcPrefixDecoder", FixedDecoder
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)
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self.patch_decoder.start()
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self.addCleanup(self.patch_decoder.stop)
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def model(self, variant, detected=True, detected2=False):
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result = (detected, "wake", 0.9)
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model = SimpleNamespace(ctc=result, ctc2=(detected2, "hello", 0.8))
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model.encode = lambda speech, lengths: (speech, lengths)
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if variant is FsmnKWSMT:
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model.encode = lambda speech, lengths: (speech, speech, lengths)
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model.encode_chunk = lambda speech, lengths, **kwargs: (speech, lengths)
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model.kws_decoder = FixedDecoder(result)
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self.addCleanup(
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lambda: model.writer.close() if hasattr(model, "writer") else None
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)
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return model
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def call(
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self, variant, model, output_dir=OMITTED, key="sample", final=True, cache=None
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):
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kwargs = {"device": "cpu", "data_type": "fbank", "keywords": "wake"}
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if output_dir is not OMITTED:
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kwargs["output_dir"] = output_dir
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speech = torch.zeros(1, 3, 1)
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lengths = torch.tensor([3])
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tokenizer = SimpleNamespace(token_list=["wake"], seg_dict={})
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if variant is FsmnKWSMT:
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tokenizer = [tokenizer, tokenizer]
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if variant is SanmKWSStreaming:
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if cache is None:
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cache = {
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"encoder": {
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"chunk_size": [0, 3, 0],
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"encoder_out": None,
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"encoder_out_lens": None,
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}
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}
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return variant.generate_chunk(
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model,
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speech,
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lengths,
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key=[key],
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tokenizer=tokenizer,
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cache=cache,
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is_final=final,
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**kwargs,
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)
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results, _ = variant.inference(
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model,
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speech,
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data_lengths=lengths[:, None],
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key=[key],
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tokenizer=tokenizer,
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**kwargs,
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)
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return results
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def expected(self, variant, detected=True, detected2=False, key="sample"):
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result = {"key": key, "text": "detected wake 0.9" if detected else "rejected"}
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if variant is FsmnKWSMT:
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result["text2"] = "detected hello 0.8" if detected2 else "rejected"
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return [result]
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def test_omitted_and_none_return_results_without_creating_writer(self):
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for variant, detected, detected2, output_dir in itertools.product(
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VARIANTS, (False, True), (False, True), (OMITTED, None)
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):
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with self.subTest(
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variant=variant.__name__,
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detected=detected,
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detected2=detected2,
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output_dir=output_dir,
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):
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model = self.model(variant, detected, detected2)
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self.assertEqual(
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self.call(variant, model, output_dir),
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self.expected(variant, detected, detected2),
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)
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self.assertFalse(hasattr(model, "writer"))
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def test_enabled_output_preserves_result_and_file_format(self):
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for variant, detected, detected2 in itertools.product(
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VARIANTS, (False, True), (False, True)
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):
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with self.subTest(
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variant=variant.__name__, detected=detected, detected2=detected2
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):
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path = (
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Path(self.directory.name)
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/ f"{variant.__name__}-{detected}-{detected2}"
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)
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model = self.model(variant, detected, detected2)
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expected = self.expected(variant, detected, detected2)
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self.assertEqual(self.call(variant, model, str(path)), expected)
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self.assertEqual(
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(path / "detect").read_text(), f"sample {expected[0]['text']}\n"
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)
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if variant is FsmnKWSMT:
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self.assertEqual(
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(path / "detect2").read_text(),
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f"sample {expected[0]['text2']}\n",
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)
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def test_disabled_output_does_not_reuse_cached_writer(self):
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for variant, output_dir in itertools.product(VARIANTS, (OMITTED, None)):
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with self.subTest(variant=variant.__name__, output_dir=output_dir):
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path = (
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Path(self.directory.name)
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/ f"{variant.__name__}-{output_dir is None}"
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)
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model = self.model(variant)
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self.call(variant, model, str(path), key="first")
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before = {p.name: p.read_bytes() for p in path.iterdir()}
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self.assertEqual(
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self.call(variant, model, output_dir, key="second"),
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self.expected(variant, key="second"),
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)
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self.assertEqual(
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{p.name: p.read_bytes() for p in path.iterdir()}, before
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)
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def test_consecutive_enabled_calls_append(self):
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for variant in VARIANTS:
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with self.subTest(variant=variant.__name__):
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path = Path(self.directory.name) / variant.__name__
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model = self.model(variant)
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for key in ("first", "second"):
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self.call(variant, model, str(path), key=key)
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self.assertEqual(
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(path / "detect").read_text(),
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"first detected wake 0.9\nsecond detected wake 0.9\n",
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)
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if variant is FsmnKWSMT:
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self.assertEqual(
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(path / "detect2").read_text(),
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"first rejected\nsecond rejected\n",
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)
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def test_streaming_nonfinal_accumulates_and_final_returns_without_output(self):
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model = self.model(SanmKWSStreaming)
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cache = {
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"encoder": {
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"chunk_size": [0, 3, 0],
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"encoder_out": None,
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"encoder_out_lens": None,
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}
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}
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self.assertIsNone(self.call(SanmKWSStreaming, model, final=False, cache=cache))
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self.assertFalse(hasattr(model, "writer"))
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self.assertEqual(
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self.call(SanmKWSStreaming, model, final=True, cache=cache),
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self.expected(SanmKWSStreaming),
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)
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self.assertEqual(cache["encoder"]["encoder_out"].shape[1], 6)
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self.assertFalse(hasattr(model, "writer"))
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if __name__ == "__main__":
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unittest.main()
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