updated model

README.md

@@ -7,4 +7,32 @@ metrics:
 - wer
 license: mit
 ---
-We took `facebook/wav2vec2-large-960h` and fine tuned it using 800 audio clips (around 8-10 seconds each) from various cryptocurrency related podcasts. To label the data, we downloaded cryptocurrency podcasts from youtube with their subtitle data and split the clips up by sentence. We then compared the youtube transcription with `facebook/wav2vec2-large-960h` to correct many mistakes in the youtube transcriptions. We can probably achieve better results with more data clean up.
+We took `facebook/wav2vec2-large-960h` and fine tuned it using 1400 audio clips (around 10-15 seconds each) from various cryptocurrency related podcasts. To label the data, we downloaded cryptocurrency podcasts from youtube with their subtitle data and split the clips up by sentence. We then compared the youtube transcription with `facebook/wav2vec2-large-960h` to correct many mistakes in the youtube transcriptions. We can probably achieve better results with more data clean up.
+
+On our data we achieved a WER of 13.1%. `facebook/wav2vec2-large-960h` only reached a WER of 27% on our data.
+
+## Usage
+```python
+from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC
+from datasets import load_dataset
+import soundfile as sf
+import torch
+
+
+# load model and tokenizer
+processor = Wav2Vec2Processor.from_pretrained("distractedm1nd/wav2vec-en-finetuned-on-cryptocurrency")
+model = Wav2Vec2ForCTC.from_pretrained("distractedm1nd/wav2vec-en-finetuned-on-cryptocurrency"
+
+filename = "INSERT_FILENAME"
+audio, sampling_rate = sf.read(filename)
+
+input_values = processor(audio, return_tensors="pt", padding="longest", sampling_rate=sampling_rate).input_values  # Batch size 1
+
+
+# retrieve logits
+logits = model(input_values).logits
+
+# take argmax and decode
+predicted_ids = torch.argmax(logits, dim=-1)
+tokenizer.batch_decode(predicted_ids
+```