def test_simple_s2t(self):
model = Speech2TextForConditionalGeneration.from_pretrained(
"facebook/s2t-small-mustc-en-it-st")
tokenizer = AutoTokenizer.from_pretrained(
"facebook/s2t-small-mustc-en-it-st")
feature_extractor = AutoFeatureExtractor.from_pretrained(
"facebook/s2t-small-mustc-en-it-st")
asr = AutomaticSpeechRecognitionPipeline(
model=model,
tokenizer=tokenizer,
feature_extractor=feature_extractor)
waveform = np.tile(np.arange(1000, dtype=np.float32), 34)
output = asr(waveform)
self.assertEqual(output, {"text": "(Applausi)"})
ds = load_dataset("hf-internal-testing/librispeech_asr_dummy",
"clean",
split="validation").sort("id")
filename = ds[40]["file"]
output = asr(filename)
self.assertEqual(
output,
{"text": "Un uomo disse all'universo: \"Signore, io esisto."})
filename = ds[40]["file"]
with open(filename, "rb") as f:
data = f.read()
output = asr(data)
self.assertEqual(
output,
{"text": "Un uomo disse all'universo: \"Signore, io esisto."})
def test_simple_wav2vec2(self):
model = Wav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-base-960h")
tokenizer = AutoTokenizer.from_pretrained(
"facebook/wav2vec2-base-960h")
feature_extractor = AutoFeatureExtractor.from_pretrained(
"facebook/wav2vec2-base-960h")
asr = AutomaticSpeechRecognitionPipeline(
model=model,
tokenizer=tokenizer,
feature_extractor=feature_extractor)
waveform = np.tile(np.arange(1000, dtype=np.float32), 34)
output = asr(waveform)
self.assertEqual(output, {"text": ""})
ds = load_dataset("hf-internal-testing/librispeech_asr_dummy",
"clean",
split="validation").sort("id")
filename = ds[40]["file"]
output = asr(filename)
self.assertEqual(output,
{"text": "A MAN SAID TO THE UNIVERSE SIR I EXIST"})
filename = ds[40]["file"]
with open(filename, "rb") as f:
data = f.read()
output = asr(data)
self.assertEqual(output,
{"text": "A MAN SAID TO THE UNIVERSE SIR I EXIST"})
def get_test_pipeline(self, model, tokenizer, feature_extractor):
if tokenizer is None:
# Side effect of no Fast Tokenizer class for these model, so skipping
# But the slow tokenizer test should still run as they're quite small
self.skipTest("No tokenizer available")
return
# return None, None
speech_recognizer = AutomaticSpeechRecognitionPipeline(
model=model,
tokenizer=tokenizer,
feature_extractor=feature_extractor)
# test with a raw waveform
audio = np.zeros((34000, ))
audio2 = np.zeros((14000, ))
return speech_recognizer, [audio, audio2]
def test_simple_s2t(self):
import numpy as np
from datasets import load_dataset
model = Speech2TextForConditionalGeneration.from_pretrained(
"facebook/s2t-small-mustc-en-it-st")
tokenizer = AutoTokenizer.from_pretrained(
"facebook/s2t-small-mustc-en-it-st")
feature_extractor = AutoFeatureExtractor.from_pretrained(
"facebook/s2t-small-mustc-en-it-st")
asr = AutomaticSpeechRecognitionPipeline(
model=model,
tokenizer=tokenizer,
feature_extractor=feature_extractor)
waveform = np.zeros((34000, ))
output = asr(waveform)
self.assertEqual(output, {
"text":
"E questo รจ il motivo per cui non ci siamo mai incontrati."
})
ds = load_dataset("patrickvonplaten/librispeech_asr_dummy",
"clean",
split="validation")
filename = ds[0]["file"]
output = asr(filename)
self.assertEqual(
output,
{"text": "Un uomo disse all'universo: \"Signore, io esisto."})
filename = ds[0]["file"]
with open(filename, "rb") as f:
data = f.read()
output = asr(data)
self.assertEqual(
output,
{"text": "Un uomo disse all'universo: \"Signore, io esisto."})
