def test_classification_json(tmpdir):
json_path = json_data(tmpdir)
data = SpeechRecognitionData.from_json(
"file",
"text",
train_file=json_path,
num_workers=0,
batch_size=2,
)
model = SpeechRecognition(backbone=TEST_BACKBONE)
trainer = Trainer(default_root_dir=tmpdir, fast_dev_run=True)
trainer.fit(model, datamodule=data)
def from_timit(
val_split: float = 0.1,
batch_size: int = 4,
num_workers: int = 0,
**input_transform_kwargs,
) -> SpeechRecognitionData:
"""Downloads and loads the timit data set."""
download_data("https://pl-flash-data.s3.amazonaws.com/timit_data.zip",
"./data")
return SpeechRecognitionData.from_json(
"file",
"text",
train_file="data/timit/train.json",
test_file="data/timit/test.json",
val_split=val_split,
batch_size=batch_size,
num_workers=num_workers,
**input_transform_kwargs,
)
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import torch
import flash
from flash.audio import SpeechRecognition, SpeechRecognitionData
from flash.core.data.utils import download_data
# 1. Create the DataModule
download_data("https://pl-flash-data.s3.amazonaws.com/timit_data.zip", "./data")
datamodule = SpeechRecognitionData.from_json(
"file",
"text",
train_file="data/timit/train.json",
test_file="data/timit/test.json",
batch_size=4,
)
# 2. Build the task
model = SpeechRecognition(backbone="facebook/wav2vec2-base-960h")
# 3. Create the trainer and finetune the model
trainer = flash.Trainer(max_epochs=1, gpus=torch.cuda.device_count())
trainer.finetune(model, datamodule=datamodule, strategy="freeze")
# 4. Predict on audio files!
datamodule = SpeechRecognitionData.from_files(predict_files=["data/timit/example.wav"], batch_size=4)
predictions = trainer.predict(model, datamodule=datamodule)
print(predictions)
def test_audio_module_not_found_error():
with pytest.raises(ModuleNotFoundError, match="[audio]"):
SpeechRecognitionData.from_json("file", "text", train_file="", batch_size=1, num_workers=0)
def test_from_json(tmpdir):
json_path = json_data(tmpdir)
dm = SpeechRecognitionData.from_json("file", "text", train_file=json_path, batch_size=1, num_workers=0)
batch = next(iter(dm.train_dataloader()))
assert DefaultDataKeys.INPUT in batch
assert DefaultDataKeys.TARGET in batch
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import torch
import flash
from flash.audio import SpeechRecognition, SpeechRecognitionData
from flash.core.data.utils import download_data
# 1. Create the DataModule
download_data("https://pl-flash-data.s3.amazonaws.com/timit_data.zip",
"./data")
datamodule = SpeechRecognitionData.from_json(
input_fields="file",
target_fields="text",
train_file="data/timit/train.json",
test_file="data/timit/test.json",
)
# 2. Build the task
model = SpeechRecognition(backbone="facebook/wav2vec2-base-960h")
# 3. Create the trainer and finetune the model
trainer = flash.Trainer(max_epochs=1, gpus=torch.cuda.device_count())
trainer.finetune(model, datamodule=datamodule, strategy="no_freeze")
# 4. Predict on audio files!
predictions = model.predict(["data/timit/example.wav"])
print(predictions)
# 5. Save the model!