Update README.md

README.md

@@ -58,79 +58,28 @@ The architecture is the same as [openai/whisper-medium](https://huggingface.co/o
 The model was trained on the Common Voice 11.0 dataset (`train+validation+other` splits) and the Romanian speech synthesis corpus, and was tested on the `test` split of the Common Voice 11.0 dataset.
 
 ## Usage
-Inference with 🤗 Pipeline
+Inference with 🤗 transformers
 ```python
+from transformers import WhisperProcessor, WhisperForConditionalGeneration
+from datasets import Audio, load_dataset
 import torch
-from datasets import load_dataset
-from transformers import pipeline
 
-device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
-# Load pipeline
-pipe = pipeline("automatic-speech-recognition", model="gigant/whisper-medium-romanian", device=device)
-# NB: set forced_decoder_ids for generation utils
-pipe.model.config.forced_decoder_ids = pipe.tokenizer.get_decoder_prompt_ids(language="ro", task="transcribe")
-
-# Load data
-ds_mcv_test = load_dataset("mozilla-foundation/common_voice_11_0", "ro", split="test", streaming=True)
-test_segment = next(iter(ds_mcv_test))
-waveform = test_segment["audio"]
-
-# NB: decoding option
-# limit the maximum number of generated tokens to 225
-pipe.model.config.max_length = 225 + 1
-# sampling
-# pipe.model.config.do_sample = True
-# beam search
-# pipe.model.config.num_beams = 5
-# return
-# pipe.model.config.return_dict_in_generate = True
-# pipe.model.config.output_scores = True
-# pipe.model.config.num_return_sequences = 5
-# Run
-generated_sentences = pipe(waveform)["text"]
-```
-Inference with 🤗 low-level APIs
-```python
-import torch
-import torchaudio
-
-from datasets import load_dataset
-from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq
-device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
-
-# Load model
-model = AutoModelForSpeechSeq2Seq.from_pretrained("gigant/whisper-medium-romanian").to(device)
-processor = AutoProcessor.from_pretrained("gigant/whisper-medium-romanian", language="romanian", task="transcribe")
-
-# NB: set forced_decoder_ids for generation utils
-model.config.forced_decoder_ids = processor.get_decoder_prompt_ids(language="ro", task="transcribe")
-# 16_000
-model_sample_rate = processor.feature_extractor.sampling_rate
-
-# Load data
-ds_mcv_test = load_dataset("mozilla-foundation/common_voice_11_0", "ro", split="test", streaming=True)
-test_segment = next(iter(ds_mcv_test))
-waveform = torch.from_numpy(test_segment["audio"]["array"])
-sample_rate = test_segment["audio"]["sampling_rate"]
-# Resample
-if sample_rate != model_sample_rate:
-    resampler = torchaudio.transforms.Resample(sample_rate, model_sample_rate)
-    waveform = resampler(waveform)
-# Get feat
-inputs = processor(waveform, sampling_rate=model_sample_rate, return_tensors="pt")
-input_features = inputs.input_features
-input_features = input_features.to(device)
-
-# Generate
-generated_ids = model.generate(inputs=input_features, max_new_tokens=225)  # greedy
-# generated_ids = model.generate(inputs=input_features, max_new_tokens=225, num_beams=5)  # beam search
-# Detokenize
-generated_sentences = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
-
-# Normalise predicted sentences if necessary
+# load model and processor
+processor = WhisperProcessor.from_pretrained("gigant/whisper-medium-romanian")
+model = WhisperForConditionalGeneration.from_pretrained("gigant/whisper-medium-romanian")
+
+# load dummy dataset and read soundfiles
+ds = load_dataset("common_voice", "ro", split="test", streaming=True)
+ds = ds.cast_column("audio", Audio(sampling_rate=16_000))
+input_speech = next(iter(ds))["audio"]["array"]
+model.config.forced_decoder_ids = processor.get_decoder_prompt_ids(language = "ro", task = "transcribe")
+input_features = processor(input_speech, return_tensors="pt", sampling_rate=16_000).input_features 
+predicted_ids = model.generate(input_features, max_length=448)
+# transcription = processor.batch_decode(predicted_ids)
+transcription = processor.batch_decode(predicted_ids, skip_special_tokens = True)
 ```
 
-The code was adapted from [bofenghuang/deprecated-whisper-large-v2-cv11-french-punct-plus](https://huggingface.co/bofenghuang/deprecated-whisper-large-v2-cv11-french-punct-plus).
+The code was adapted from [openai/whisper-medium](https://huggingface.co/openai/whisper-medium).
 
 ## Training procedure