Resolve audio CI (#1368)

Lightning-Universe · Jun 29, 2022 · 345c95c · 345c95c
1 parent e380b02
commit 345c95c
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Showing 2 changed files with 9 additions and 9 deletions.
diff --git a/.github/workflows/ci-testing.yml b/.github/workflows/ci-testing.yml
@@ -135,7 +135,7 @@ jobs:
       run: |
         sudo apt-get install libsndfile1
         pip install matplotlib
-        pip install '.[audio,image]' --upgrade
+        pip install '.[audio,image]' torch==1.11.0 --upgrade
 
     - name: Cache datasets
       uses: actions/cache@v2

diff --git a/flash/audio/speech_recognition/data.py b/flash/audio/speech_recognition/data.py
@@ -88,8 +88,8 @@ def from_files(
 
             >>> import numpy as np
             >>> import soundfile as sf
-            >>> samplerate = 44100
-            >>> data = np.random.uniform(-1, 1, size=(samplerate * 3, 2))
+            >>> samplerate = 1000
+            >>> data = np.random.uniform(-1, 1, size=(samplerate, 2))
             >>> _ = [sf.write(f"speech_{i}.wav", data, samplerate, subtype='PCM_24') for i in range(1, 4)]
             >>> _ = [sf.write(f"predict_speech_{i}.wav", data, samplerate, subtype='PCM_24') for i in range(1, 4)]
 
@@ -180,8 +180,8 @@ def from_csv(
             >>> import numpy as np
             >>> from pandas import DataFrame
             >>> import soundfile as sf
-            >>> samplerate = 44100
-            >>> data = np.random.uniform(-1, 1, size=(samplerate * 3, 2))
+            >>> samplerate = 1000
+            >>> data = np.random.uniform(-1, 1, size=(samplerate, 2))
             >>> _ = [sf.write(f"speech_{i}.wav", data, samplerate, subtype='PCM_24') for i in range(1, 4)]
             >>> _ = [sf.write(f"predict_speech_{i}.wav", data, samplerate, subtype='PCM_24') for i in range(1, 4)]
             >>> DataFrame.from_dict({
@@ -304,8 +304,8 @@ def from_json(
             >>> import numpy as np
             >>> from pandas import DataFrame
             >>> import soundfile as sf
-            >>> samplerate = 44100
-            >>> data = np.random.uniform(-1, 1, size=(samplerate * 3, 2))
+            >>> samplerate = 1000
+            >>> data = np.random.uniform(-1, 1, size=(samplerate, 2))
             >>> _ = [sf.write(f"speech_{i}.wav", data, samplerate, subtype='PCM_24') for i in range(1, 4)]
             >>> _ = [sf.write(f"predict_speech_{i}.wav", data, samplerate, subtype='PCM_24') for i in range(1, 4)]
             >>> DataFrame.from_dict({
@@ -423,8 +423,8 @@ def from_datasets(
 
             >>> import numpy as np
             >>> import soundfile as sf
-            >>> samplerate = 44100
-            >>> data = np.random.uniform(-1, 1, size=(samplerate * 3, 2))
+            >>> samplerate = 1000
+            >>> data = np.random.uniform(-1, 1, size=(samplerate, 2))
             >>> _ = [sf.write(f"speech_{i}.wav", data, samplerate, subtype='PCM_24') for i in range(1, 4)]
             >>> _ = [sf.write(f"predict_speech_{i}.wav", data, samplerate, subtype='PCM_24') for i in range(1, 4)]