Added inference

1 files changed, 0 insertions, 75 deletions

diff --git a/swr2_asr/inference_test.py b/swr2_asr/inference_test.py
deleted file mode 100644
index 96277fd..0000000
--- a/swr2_asr/inference_test.py
+++ /dev/null
@@ -1,75 +0,0 @@
-"""Training script for the ASR model."""
-import torch
-import torchaudio
-import torchaudio.functional as F
-
-
-class GreedyCTCDecoder(torch.nn.Module):
-    """Greedy CTC decoder for the wav2vec2 model."""
-
-    def __init__(self, labels, blank=0) -> None:
-        super().__init__()
-        self.labels = labels
-        self.blank = blank
-
-    def forward(self, emission: torch.Tensor) -> str:
-        """Given a sequence emission over labels, get the best path string
-        Args:
-          emission (Tensor): Logit tensors. Shape `[num_seq, num_label]`.
-
-        Returns:
-          str: The resulting transcript
-        """
-        indices = torch.argmax(emission, dim=-1)  # [num_seq,]
-        indices = torch.unique_consecutive(indices, dim=-1)
-        indices = [i for i in indices if i != self.blank]
-        return "".join([self.labels[i] for i in indices])
-
-
-'''
-Sorry marvin, Please fix this to use the new dataset
-def main() -> None:
-    """Main function."""
-    # choose between cuda, cpu and mps devices
-    device = "cuda" if torch.cuda.is_available() else "cpu"
-    # device = "mps"
-    device = torch.device(device)
-
-    torch.random.manual_seed(42)
-
-    bundle = torchaudio.pipelines.WAV2VEC2_ASR_BASE_960H
-
-    print(f"Sample rate (model): {bundle.sample_rate}")
-    print(f"Labels (model): {bundle.get_labels()}")
-
-    model = bundle.get_model().to(device)
-
-    print(model.__class__)
-
-    # only do all things for one single sample
-    dataset = MultilingualLibriSpeech("data", "mls_german_opus", split="train", download=True)
-
-    print(dataset[0])
-
-    # load waveforms and sample rate from dataset
-    waveform, sample_rate = dataset[0]["waveform"], dataset[0]["sample_rate"]
-
-    if sample_rate != bundle.sample_rate:
-        waveform = F.resample(waveform, sample_rate, int(bundle.sample_rate))
-
-    waveform.to(device)
-
-    with torch.inference_mode():
-        features, _ = model.extract_features(waveform)
-
-    with torch.inference_mode():
-        emission, _ = model(waveform)
-
-    decoder = GreedyCTCDecoder(labels=bundle.get_labels())
-    transcript = decoder(emission[0])
-
-    print(transcript)
-'''
-
-if __name__ == "__main__":
-    main()