Create asr.py

asr.py

@@ -0,0 +1,57 @@
+from transformers import Wav2Vec2ForCTC, AutoProcessor
+import torch
+from transformers import Wav2Vec2ForSequenceClassification, AutoFeatureExtractor
+import time
+import gradio as gr
+import librosa
+
+model_id = "facebook/mms-1b-all"
+processor = AutoProcessor.from_pretrained(model_id)
+model = Wav2Vec2ForCTC.from_pretrained(model_id)
+
+model_id_lid = "facebook/mms-lid-126"
+processor_lid = AutoFeatureExtractor.from_pretrained(model_id_lid)
+model_lid = Wav2Vec2ForSequenceClassification.from_pretrained(model_id_lid)
+
+def transcribe(audio):
+    audio = librosa.load(audio, sr=16_000, mono=True)[0]
+    inputs = processor(audio, sampling_rate=16_000,return_tensors="pt")
+    with torch.no_grad():
+        tr_start_time = time.time()
+        outputs = model(**inputs).logits
+        tr_end_time = time.time()
+    ids = torch.argmax(outputs, dim=-1)[0]
+    transcription = processor.decode(ids)
+    return transcription,(tr_end_time-tr_start_time)
+
+
+def detect_language(audio):  
+    audio = librosa.load(audio, sr=16_000, mono=True)[0]
+    # print(audio)
+    inputs_lid = processor_lid(audio, sampling_rate=16_000, return_tensors="pt")
+    with torch.no_grad():
+        start_time_lid = time.time()
+        outputs_lid = model_lid(**inputs_lid).logits
+        end_time = time.time()
+#     print(end_time-start_time," sec")
+    lang_id = torch.argmax(outputs_lid, dim=-1)[0].item()
+    detected_lang = model_lid.config.id2label[lang_id]
+    print(detected_lang)
+    return detected_lang, (end_time_lid-start_time_lid)
+
+
+def transcribe_lang(audio,lang):
+    audio = librosa.load(audio, sr=16_000, mono=True)[0]
+    processor.tokenizer.set_target_lang(lang)
+    model.load_adapter(lang)
+    print(lang)
+    inputs = processor(audio, sampling_rate=16_000,return_tensors="pt")
+    with torch.no_grad():
+        tr_start_time = time.time()
+        outputs = model(**inputs).logits
+        tr_end_time = time.time()
+    ids = torch.argmax(outputs, dim=-1)[0]
+    transcription = processor.decode(ids)
+    return transcription,(tr_end_time-tr_start_time)
+
+