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README.md

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+---
+language: 
+- en
+- zh
+- de
+- es
+- ru
+- ko
+- fr
+- ja
+- pt
+- tr
+- pl
+- ca
+- nl
+- ar
+- sv
+- it
+- id
+- hi
+- fi
+- vi
+- he
+- uk
+- el
+- ms
+- cs
+- ro
+- da
+- hu
+- ta
+- no
+- th
+- ur
+- hr
+- bg
+- lt
+- la
+- mi
+- ml
+- cy
+- sk
+- te
+- fa
+- lv
+- bn
+- sr
+- az
+- sl
+- kn
+- et
+- mk
+- br
+- eu
+- is
+- hy
+- ne
+- mn
+- bs
+- kk
+- sq
+- sw
+- gl
+- mr
+- pa
+- si
+- km
+- sn
+- yo
+- so
+- af
+- oc
+- ka
+- be
+- tg
+- sd
+- gu
+- am
+- yi
+- lo
+- uz
+- fo
+- ht
+- ps
+- tk
+- nn
+- mt
+- sa
+- lb
+- my
+- bo
+- tl
+- mg
+- as
+- tt
+- haw
+- ln
+- ha
+- ba
+- jw
+- su
+tags:
+- audio
+- automatic-speech-recognition
+- open-persian-asr-leaderboard
+pipeline_tag: automatic-speech-recognition
+license: apache-2.0
+---
+
+# C1Tech/whisper_base_persian
+
+*C1Tech/whisper_base_persian* is a Persian ASR model based on Whisper architecture, fine-tuned on a large scale custom persian dataset. 
+
+With only 74 million parameters, this model achieves state-of-the-art performance on Persian ASR tasks, outperforming larger models like openai Whisper Large-v3 (1550M parameters) and Meta Wav2Vec2-XLSR (300M parameters).
+
+## Model Details
+We evaluated the model on multiple Persian ASR benchmarks, including Common Voice, and fleurs. Results show that our model outperforms popular models like vosk, fast-conformer and openai's whisper on these benchmarks:
+<p align="center" style="display: flex; justify-content: space-around;">  
+<img src="assets/base_common-voice.png" alt="Model Image 1" width="45%" />
+  <img src="assets/base_fleurs.png" alt="Model Image 2" width="45%" />
+</p>
+
+## Usage
+
+Whisper base is supported in Hugging Face 🤗 Transformers. To run the model, first install the Transformers 
+library. 
+
+```bash
+pip install --upgrade pip
+pip install --upgrade transformers
+```
+
+The model can be used with the [`pipeline`](https://huggingface.co/docs/transformers/main_classes/pipelines#transformers.AutomaticSpeechRecognitionPipeline)
+class to transcribe audios of arbitrary length:
+
+```python
+import torch
+from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor, pipeline
+
+
+device = "cuda:0" if torch.cuda.is_available() else "cpu"
+torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32
+
+model_id = "C1Tech/whisper_base_persian"
+
+model = AutoModelForSpeechSeq2Seq.from_pretrained(
+    model_id, torch_dtype=torch_dtype, low_cpu_mem_usage=True, use_safetensors=True
+)
+model.to(device)
+
+processor = AutoProcessor.from_pretrained(model_id)
+
+pipe = pipeline(
+    "automatic-speech-recognition",
+    model=model,
+    tokenizer=processor.tokenizer,
+    feature_extractor=processor.feature_extractor,
+    torch_dtype=torch_dtype,
+    device=device,
+)
+
+```
+
+To transcribe a local audio file, simply pass the path to your audio file when you call the pipeline:
+
+```python
+result = pipe("audio.mp3")
+```
+
+Multiple audio files can be transcribed in parallel by specifying them as a list and setting the `batch_size` parameter:
+
+```python
+result = pipe(["audio_1.mp3", "audio_2.mp3"], batch_size=2)
+```
+
+Transformers is compatible with all Whisper decoding strategies, such as temperature fallback and condition on previous 
+tokens. The following example demonstrates how to enable these heuristics:
+
+```python
+generate_kwargs = {
+    "num_beams": 3,
+    "condition_on_prev_tokens": False,
+    "compression_ratio_threshold": 1.35,  # zlib compression ratio threshold (in token space)
+    "temperature": (0.0, 0.2, 0.4, 0.6, 0.8, 1.0),
+    "logprob_threshold": -1.0,
+    "no_speech_threshold": 0.6,
+    "return_timestamps": True,
+    "language": "fa"
+}
+
+result = pipe(sample, generate_kwargs=generate_kwargs)
+```
+
+Finally, the model can be made to predict timestamps. For sentence-level timestamps, pass the `return_timestamps` argument:
+
+```python
+result = pipe(sample, return_timestamps=True)
+print(result["chunks"])
+```
+
+And for word-level timestamps:
+
+```python
+result = pipe(sample, return_timestamps="word")
+print(result["chunks"])
+```